| 1. |
- Hibar, Derrek P., et al.
(författare)
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Novel genetic loci associated with hippocampal volume
- 2017
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (r(g) = -0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness.
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| 2. |
- Satizabal, Claudia L., et al.
(författare)
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Genetic architecture of subcortical brain structures in 38,851 individuals
- 2019
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Ingår i: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 51:11, s. 1624-
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Tidskriftsartikel (refereegranskat)abstract
- Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.
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| 3. |
- Dima, Danai, et al.
(författare)
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Subcortical volumes across the lifespan : Data from 18,605 healthy individuals aged 3-90 years.
- 2022
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Ingår i: Human Brain Mapping. - : Wiley. - 1065-9471 .- 1097-0193. ; 43:1, s. 452-469
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Tidskriftsartikel (refereegranskat)abstract
- Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.
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| 4. |
- Frangou, Sophia, et al.
(författare)
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Cortical thickness across the lifespan : Data from 17,075 healthy individuals aged 3-90 years
- 2022
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Ingår i: Human Brain Mapping. - : John Wiley & Sons. - 1065-9471 .- 1097-0193. ; 43:1, s. 431-451
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Tidskriftsartikel (refereegranskat)abstract
- Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
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| 5. |
- Boen, Rune, et al.
(författare)
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Beyond the global brain differences : intraindividual variability differences in 1q21.1 distal and 15q11.2 bp1-bp2 deletion carriers
- 2024
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Ingår i: Biological Psychiatry. - 0006-3223 .- 1873-2402. ; 95:2, s. 147-160
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Tidskriftsartikel (refereegranskat)abstract
- Background: Carriers of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants exhibit regional and global brain differences compared with noncarriers. However, interpreting regional differences is challenging if a global difference drives the regional brain differences. Intraindividual variability measures can be used to test for regional differences beyond global differences in brain structure.Methods: Magnetic resonance imaging data were used to obtain regional brain values for 1q21.1 distal deletion (n = 30) and duplication (n = 27) and 15q11.2 BP1-BP2 deletion (n = 170) and duplication (n = 243) carriers and matched noncarriers (n = 2350). Regional intra-deviation scores, i.e., the standardized difference between an individual's regional difference and global difference, were used to test for regional differences that diverge from the global difference.Results: For the 1q21.1 distal deletion carriers, cortical surface area for regions in the medial visual cortex, posterior cingulate, and temporal pole differed less and regions in the prefrontal and superior temporal cortex differed more than the global difference in cortical surface area. For the 15q11.2 BP1-BP2 deletion carriers, cortical thickness in regions in the medial visual cortex, auditory cortex, and temporal pole differed less and the prefrontal and somatosensory cortex differed more than the global difference in cortical thickness.Conclusions: We find evidence for regional effects beyond differences in global brain measures in 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants. The results provide new insight into brain profiling of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants, with the potential to increase understanding of the mechanisms involved in altered neurodevelopment.
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| 6. |
- Erzurumluoglu, A. Mesut, et al.
(författare)
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Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci
- 2020
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Ingår i: Molecular Psychiatry. - : Nature Publishing Group. - 1359-4184 .- 1476-5578. ; 25:10, s. 2392-2409
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Tidskriftsartikel (refereegranskat)abstract
- Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10-8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10-8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10-3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.
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| 7. |
- Surendran, Praveen, et al.
(författare)
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Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals
- 2020
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Ingår i: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 52:12, s. 1314-1332
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Tidskriftsartikel (refereegranskat)abstract
- Genetic studies of blood pressure (BP) to date have mainly analyzed common variants (minor allele frequency > 0.05). In a meta-analysis of up to similar to 1.3 million participants, we discovered 106 new BP-associated genomic regions and 87 rare (minor allele frequency <= 0.01) variant BP associations (P < 5 x 10(-8)), of which 32 were in new BP-associated loci and 55 were independent BP-associated single-nucleotide variants within known BP-associated regions. Average effects of rare variants (44% coding) were similar to 8 times larger than common variant effects and indicate potential candidate causal genes at new and known loci (for example, GATA5 and PLCB3). BP-associated variants (including rare and common) were enriched in regions of active chromatin in fetal tissues, potentially linking fetal development with BP regulation in later life. Multivariable Mendelian randomization suggested possible inverse effects of elevated systolic and diastolic BP on large artery stroke. Our study demonstrates the utility of rare-variant analyses for identifying candidate genes and the results highlight potential therapeutic targets.
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| 8. |
- Sønderby, Ida E., et al.
(författare)
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1q21.1 distal copy number variants are associated with cerebral and cognitive alterations in humans
- 2021
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Ingår i: Translational Psychiatry. - : Nature Publishing Group. - 2158-3188. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Low-frequency 1q21.1 distal deletion and duplication copy number variant (CNV) carriers are predisposed to multiple neurodevelopmental disorders, including schizophrenia, autism and intellectual disability. Human carriers display a high prevalence of micro- and macrocephaly in deletion and duplication carriers, respectively. The underlying brain structural diversity remains largely unknown. We systematically called CNVs in 38 cohorts from the large-scale ENIGMA-CNV collaboration and the UK Biobank and identified 28 1q21.1 distal deletion and 22 duplication carriers and 37,088 non-carriers (48% male) derived from 15 distinct magnetic resonance imaging scanner sites. With standardized methods, we compared subcortical and cortical brain measures (all) and cognitive performance (UK Biobank only) between carrier groups also testing for mediation of brain structure on cognition. We identified positive dosage effects of copy number on intracranial volume (ICV) and total cortical surface area, with the largest effects in frontal and cingulate cortices, and negative dosage effects on caudate and hippocampal volumes. The carriers displayed distinct cognitive deficit profiles in cognitive tasks from the UK Biobank with intermediate decreases in duplication carriers and somewhat larger in deletion carriers-the latter potentially mediated by ICV or cortical surface area. These results shed light on pathobiological mechanisms of neurodevelopmental disorders, by demonstrating gene dose effect on specific brain structures and effect on cognitive function.
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| 9. |
- van der Meer, Dennis, et al.
(författare)
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Association of Copy Number Variation of the 15q11.2 BP1-BP2 Region With Cortical and Subcortical Morphology and Cognition
- 2020
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Ingår i: JAMA psychiatry. - : American Medical Association (AMA). - 2168-6238 .- 2168-622X. ; 77:4, s. 420-430
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Tidskriftsartikel (refereegranskat)abstract
- Importance: Recurrent microdeletions and duplications in the genomic region 15q11.2 between breakpoints 1 (BP1) and 2 (BP2) are associated with neurodevelopmental disorders. These structural variants are present in 0.5% to 1.0% of the population, making 15q11.2 BP1-BP2 the site of the most prevalent known pathogenic copy number variation (CNV). It is unknown to what extent this CNV influences brain structure and affects cognitive abilities.Objective: To determine the association of the 15q11.2 BP1-BP2 deletion and duplication CNVs with cortical and subcortical brain morphology and cognitive task performance.Design, Setting, and Participants: In this genetic association study, T1-weighted brain magnetic resonance imaging were combined with genetic data from the ENIGMA-CNV consortium and the UK Biobank, with a replication cohort from Iceland. In total, 203 deletion carriers, 45 247 noncarriers, and 306 duplication carriers were included. Data were collected from August 2015 to April 2019, and data were analyzed from September 2018 to September 2019.Main Outcomes and Measures: The associations of the CNV with global and regional measures of surface area and cortical thickness as well as subcortical volumes were investigated, correcting for age, age2, sex, scanner, and intracranial volume. Additionally, measures of cognitive ability were analyzed in the full UK Biobank cohort.Results: Of 45 756 included individuals, the mean (SD) age was 55.8 (18.3) years, and 23 754 (51.9%) were female. Compared with noncarriers, deletion carriers had a lower surface area (Cohen d = -0.41; SE, 0.08; P = 4.9 × 10-8), thicker cortex (Cohen d = 0.36; SE, 0.07; P = 1.3 × 10-7), and a smaller nucleus accumbens (Cohen d = -0.27; SE, 0.07; P = 7.3 × 10-5). There was also a significant negative dose response on cortical thickness (β = -0.24; SE, 0.05; P = 6.8 × 10-7). Regional cortical analyses showed a localization of the effects to the frontal, cingulate, and parietal lobes. Further, cognitive ability was lower for deletion carriers compared with noncarriers on 5 of 7 tasks.Conclusions and Relevance: These findings, from the largest CNV neuroimaging study to date, provide evidence that 15q11.2 BP1-BP2 structural variation is associated with brain morphology and cognition, with deletion carriers being particularly affected. The pattern of results fits with known molecular functions of genes in the 15q11.2 BP1-BP2 region and suggests involvement of these genes in neuronal plasticity. These neurobiological effects likely contribute to the association of this CNV with neurodevelopmental disorders.
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| 10. |
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| 11. |
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| 12. |
- Sonderby, Ida E., et al.
(författare)
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Dose response of the 16p11.2 distal copy number variant on intracranial volume and basal ganglia
- 2020
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Ingår i: Molecular Psychiatry. - : Nature Publishing Group. - 1359-4184 .- 1476-5578. ; 25:3, s. 584-602
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Tidskriftsartikel (refereegranskat)abstract
- Carriers of large recurrent copy number variants (CNVs) have a higher risk of developing neurodevelopmental disorders. The 16p11.2 distal CNV predisposes carriers to e.g., autism spectrum disorder and schizophrenia. We compared subcortical brain volumes of 12 16p11.2 distal deletion and 12 duplication carriers to 6882 non-carriers from the large-scale brain Magnetic Resonance Imaging collaboration, ENIGMA-CNV. After stringent CNV calling procedures, and standardized FreeSurfer image analysis, we found negative dose-response associations with copy number on intracranial volume and on regional caudate, pallidum and putamen volumes (β = −0.71 to −1.37; P < 0.0005). In an independent sample, consistent results were obtained, with significant effects in the pallidum (β = −0.95, P = 0.0042). The two data sets combined showed significant negative dose-response for the accumbens, caudate, pallidum, putamen and ICV (P = 0.0032, 8.9 × 10−6, 1.7 × 10−9, 3.5 × 10−12 and 1.0 × 10−4, respectively). Full scale IQ was lower in both deletion and duplication carriers compared to non-carriers. This is the first brain MRI study of the impact of the 16p11.2 distal CNV, and we demonstrate a specific effect on subcortical brain structures, suggesting a neuropathological pattern underlying the neurodevelopmental syndromes.
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| 13. |
- Wierenga, Lara M., et al.
(författare)
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Greater male than female variability in regional brain structure across the lifespan
- 2022
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Ingår i: Human Brain Mapping. - : John Wiley & Sons. - 1065-9471 .- 1097-0193. ; 43:1, s. 470-499
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Tidskriftsartikel (refereegranskat)abstract
- For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.
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| 14. |
- Alnaes, Dag, et al.
(författare)
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Brain Heterogeneity in Schizophrenia and Its Association With Polygenic Risk
- 2019
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Ingår i: JAMA psychiatry. - : AMER MEDICAL ASSOC. - 2168-6238 .- 2168-622X. ; 76:7, s. 739-748
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Tidskriftsartikel (refereegranskat)abstract
- ImportanceBetween-individual variability in brain structure is determined by gene-environment interactions, possibly reflecting differential sensitivity to environmental and genetic perturbations. Magnetic resonance imaging (MRI) studies have revealed thinner cortices and smaller subcortical volumes in patients with schizophrenia. However, group-level comparisons may mask considerable within-group heterogeneity, which has largely remained unnoticed in the literature. ObjectivesTo compare brain structural variability between individuals with schizophrenia and healthy controls and to test whether respective variability reflects the polygenic risk score (PRS) for schizophrenia in an independent sample of healthy controls. Design, Setting, and ParticipantsThis case-control and polygenic risk analysis compared MRI-derived cortical thickness and subcortical volumes between healthy controls and patients with schizophrenia across 16 cohorts and tested for associations between PRS and MRI features in a control cohort from the UK Biobank. Data were collected from October 27, 2004, through April 12, 2018, and analyzed from December 3, 2017, through August 1, 2018. Main Outcomes and MeasuresMean and dispersion parameters were estimated using double generalized linear models. Vertex-wise analysis was used to assess cortical thickness, and regions-of-interest analyses were used to assess total cortical volume, total surface area, and white matter, subcortical, and hippocampal subfield volumes. Follow-up analyses included within-sample analysis, test of robustness of the PRS threshold, population covariates, outlier removal, and control for image quality. ResultsA comparison of 1151 patients with schizophrenia (mean [SD] age,33.8[10.6] years; 68.6% male [n=790] and 31.4% female [n=361]) with 2010 healthy controls (mean [SD] age,32.6[10.4] years; 56.0% male [n=1126] and 44.0% female [n=884]) revealed higher heterogeneity in schizophrenia for cortical thickness and area (t = 3.34), cortical (t=3.24) and ventricle (t range, 3.15-5.78) volumes, and hippocampal subfields (t range, 2.32-3.55). In the UK Biobank sample of 12 490 participants (mean [SD] age,55.9 [7.5] years; 48.2% male [n=6025] and 51.8% female [n=6465]), higher PRS was associated with thinner frontal and temporal cortices and smaller left CA2/3 (t=-3.00) but was not significantly associated with dispersion. Conclusions and RelevanceThis study suggests that schizophrenia is associated with substantial brain structural heterogeneity beyond the mean differences. These findings may reflect higher sensitivity to environmental and genetic perturbations in patients, supporting the heterogeneous nature of schizophrenia. A higher PRS was associated with thinner frontotemporal cortices and smaller hippocampal subfield volume, but not heterogeneity. This finding suggests that brain variability in schizophrenia results from interactions between environmental and genetic factors that are not captured by the PRS. Factors contributing to heterogeneity in frontotemporal cortices and hippocampus are key to furthering our understanding of how genetic and environmental factors shape brain biology in schizophrenia.
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| 15. |
- Beck, Dani, et al.
(författare)
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Dissecting unique and common variance across body and brain health indicators using age prediction
- 2024
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Ingår i: Human Brain Mapping. - : WILEY. - 1065-9471 .- 1097-0193. ; 45:6
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Tidskriftsartikel (refereegranskat)abstract
- Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals. A 'body age' model trained on health traits demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. Health traits may differentially influence age predictions beyond what is captured by the brain imaging data, revealing a degree of unique variance in brain and bodily ageing processes. image
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| 16. |
- Córdova-Palomera, Aldo, et al.
(författare)
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Genetic control of variability in subcortical and intracranial volumes
- 2021
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Ingår i: Molecular Psychiatry. - : Nature Publishing Group. - 1359-4184 .- 1476-5578. ; 26:8, s. 3876-3883
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Tidskriftsartikel (refereegranskat)abstract
- Sensitivity to external demands is essential for adaptation to dynamic environments, but comes at the cost of increased risk of adverse outcomes when facing poor environmental conditions. Here, we apply a novel methodology to perform genome-wide association analysis of mean and variance in ten key brain features (accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, intracranial volume, cortical surface area, and cortical thickness), integrating genetic and neuroanatomical data from a large lifespan sample (n = 25,575 individuals; 8-89 years, mean age 51.9 years). We identify genetic loci associated with phenotypic variability in thalamus volume and cortical thickness. The variance-controlling loci involved genes with a documented role in brain and mental health and were not associated with the mean anatomical volumes. This proof-of-principle of the hypothesis of a genetic regulation of brain volume variability contributes to establishing the genetic basis of phenotypic variance (i.e., heritability), allows identifying different degrees of brain robustness across individuals, and opens new research avenues in the search for mechanisms controlling brain and mental health.
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| 17. |
- Fachrizal, Reza, 1993-, et al.
(författare)
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Direct forecast of solar irradiance for EV smartcharging scheme to improve PV self-consumptionat home
- 2021
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Ingår i: 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665448758
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Konferensbidrag (refereegranskat)abstract
- The integration of electric vehicle (EV) chargingand Photovoltaic (PV) systems at residential buildings has increased in recent years and poses new challenges for the power system. Smart charging of EVs is believed to be one ofthe solutions to problems arising from PV and EV integration since it can improve the synergy between PV generation and EV charging. Accurate forecasts of PV generation plays an important role in smart charging schemes to optimally utilize the PV electricity for EV charging. This paper presents an assessment of a direct forecasting method applied to an EV smart charging scheme. Direct forecasting is a forecasting method which focus directly on the link between the forecast origin and the targeted horizon. The objective of the smart charging in this study is to minimize the net-load variability, which will also increase the self-consumption of PV electricity and reduce the peak loads. The PV self-consumption ratios in different forecast scenarios are compared. Results show that the smart charging with the direct forecast can achieve up to 89% of the PV self-consumption performance of the scheme with perfect forecast
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| 18. |
- Fachrizal, Reza, 1993-, et al.
(författare)
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Smart charging of electric vehicles considering photovoltaic power production and electricity consumption : a review
- 2020
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Ingår i: eTransporation. - : Elsevier. - 2590-1168. ; 4
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Forskningsöversikt (refereegranskat)abstract
- Photovoltaics (PV) and electric vehicles (EVs) are two emerging technologies often considered as cornerstones in the energy and transportation systems of future sustainable cities. They both have to be integrated into the power systems and be operated together with already existing loads and generators and, often, into buildings, where they potentially impact the overall energy performance of the buildings. Thus, a high penetration of both PV and EVs poses new challenges. Understanding of the synergies between PV, EVs and existing electricity consumption is therefore required. Recent research has shown that smart charging of EVs could improve the synergy between PV, EVs and electricity consumption, leading to both technical and economic advantages. Considering the growing interest in this field, this review paper summarizes state-of-the-art studies of smart charging considering PV power production and electricity consumption. The main aspects of smart charging reviewed are objectives, configurations, algorithms and mathematical models. Various charging objectives, such as increasing PV utilization and reducing peak loads and charging cost, are reviewed in this paper. The different charging control configurations, i.e., centralized and distributed, along with various spatial configurations, e.g., houses and workplaces, are also discussed. After that, the commonly employed optimization techniques and rule-based algorithms for smart charging are reviewed. Further research should focus on finding optimal trade-offs between simplicity and performance of smart charging schemes in terms of control configuration, charging algorithms, as well as the inclusion of PV power and load forecast in order to make the schemes suitable for practical implementations.
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| 19. |
- Frimane, Âzeddine, 1990-, et al.
(författare)
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Infinite hidden Markov model for short-term solar irradiance forecasting
- 2022
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Ingår i: Solar Energy. - : Elsevier. - 0038-092X .- 1471-1257. ; 244, s. 331-342
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Tidskriftsartikel (refereegranskat)abstract
- Hidden state models are among the most widely used and efficient schemes for solar irradiance modeling in general and forecasting in particular. However, the complexity of such models – in terms of the number of states – is usually needed to be specified a priori. For solar irradiance data this assumption is very difficult to justify.In this paper, an infinite hidden Markov model (InfHMM) is introduced for short-term probabilistic forecasting of solar irradiance, where the assumption of fixed number of states a priori is relaxed and model complexity is determined during the model training. InfHMM is a non-parametric Bayesian model (NPB) indexed with an infinite dimensional parameter space which allows the automatic adaptation of the model to the “correct” complexity. This facilitates the automatic adaptation of the model to all weather conditions and locations. Posterior inference for InfHMM is performed using the Markov chain Monte Carlo algorithm, namely the beam sampler.Data from 13 different sources are used to validate the proposed model and subsequently it is compared to two well-established models in the literature: Markov-chain mixture distribution (MCM) and complete-history persistence ensemble (CH-PeEn) models. Important results are found, that cannot be derived from the existing finite models, such as the variation of the number of states within and across sites. The comparison of the models shows that the InfHMM is more consistent in term of the forecasting horizon.For reproducibility of the methodology presented in this paper, we have provided an R script for the InfHMM as supplementary material.
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| 20. |
- Gurholt, Tiril P., et al.
(författare)
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Linking sarcopenia, brain structure and cognitive performance: a large-scale UK Biobank study
- 2024
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Ingår i: Brain Communications. - : OXFORD UNIV PRESS. - 2632-1297. ; 6:2
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Tidskriftsartikel (refereegranskat)abstract
- Sarcopenia refers to age-related loss of muscle mass and function and is related to impaired somatic and brain health, including cognitive decline and Alzheimer's disease. However, the relationships between sarcopenia, brain structure and cognition are poorly understood. Here, we investigate the associations between sarcopenic traits, brain structure and cognitive performance. We included 33 709 UK Biobank participants (54.2% female; age range 44-82 years) with structural and diffusion magnetic resonance imaging, thigh muscle fat infiltration (n = 30 561) from whole-body magnetic resonance imaging (muscle quality indicator) and general cognitive performance as indicated by the first principal component of a principal component analysis across multiple cognitive tests (n = 22 530). Of these, 1703 participants qualified for probable sarcopenia based on low handgrip strength, and we assigned the remaining 32 006 participants to the non-sarcopenia group. We used multiple linear regression to test how sarcopenic traits (probable sarcopenia versus non-sarcopenia and percentage of thigh muscle fat infiltration) relate to cognitive performance and brain structure (cortical thickness and area, white matter fractional anisotropy and deep and lower brain volumes). Next, we used structural equation modelling to test whether brain structure mediated the association between sarcopenic and cognitive traits. We adjusted all statistical analyses for confounders. We show that sarcopenic traits (probable sarcopenia versus non-sarcopenia and muscle fat infiltration) are significantly associated with lower cognitive performance and various brain magnetic resonance imaging measures. In probable sarcopenia, for the included brain regions, we observed widespread significant lower white matter fractional anisotropy (77.1% of tracts), predominantly lower regional brain volumes (61.3% of volumes) and thinner cortical thickness (37.9% of parcellations), with |r| effect sizes in (0.02, 0.06) and P-values in (0.0002, 4.2e(-29)). In contrast, we observed significant associations between higher muscle fat infiltration and widespread thinner cortical thickness (76.5% of parcellations), lower white matter fractional anisotropy (62.5% of tracts) and predominantly lower brain volumes (35.5% of volumes), with |r| effect sizes in (0.02, 0.07) and P-values in (0.0002, 1.9e(-31)). The regions showing the most significant effect sizes across the cortex, white matter and volumes were of the sensorimotor system. Structural equation modelling analysis revealed that sensorimotor brain regions mediate the link between sarcopenic and cognitive traits [probable sarcopenia: P-values in (0.0001, 1.0e-11); muscle fat infiltration: P-values in (7.7e(-05), 1.7e(-12))]. Our findings show significant associations between sarcopenic traits, brain structure and cognitive performance in a middle-aged and older adult population. Mediation analyses suggest that regional brain structure mediates the association between sarcopenic and cognitive traits, with potential implications for dementia development and prevention.
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| 21. |
- Gurholt, Tiril P., et al.
(författare)
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Population-based body-brain mapping links brain morphology with anthropometrics and body composition
- 2021
-
Ingår i: Translational Psychiatry. - : Springer Nature. - 2158-3188. ; 11:1
-
Tidskriftsartikel (refereegranskat)abstract
- Understanding complex body-brain processes and the interplay between adipose tissue and brain health is important for understanding comorbidity between psychiatric and cardiometabolic disorders. We investigated associations between brain structure and anthropometric and body composition measures using brain magnetic resonance imaging (MRI; n=24,728) and body MRI (n=4973) of generally healthy participants in the UK Biobank. We derived regional and global measures of brain morphometry using FreeSurfer and tested their association with (i) anthropometric measures, and (ii) adipose and muscle tissue measured from body MRI. We identified several significant associations with small effect sizes. Anthropometric measures showed negative, nonlinear, associations with cerebellar/cortical gray matter, and brain stem structures, and positive associations with ventricular volumes. Subcortical structures exhibited mixed effect directionality, with strongest positive association for accumbens. Adipose tissue measures, including liver fat and muscle fat infiltration, were negatively associated with cortical/cerebellum structures, while total thigh muscle volume was positively associated with brain stem and accumbens. Regional investigations of cortical area, thickness, and volume indicated widespread and largely negative associations with anthropometric and adipose tissue measures, with an opposite pattern for thigh muscle volume. Self-reported diabetes, hypertension, or hypercholesterolemia were associated with brain structure. The findings provide new insight into physiological body-brain associations suggestive of shared mechanisms between cardiometabolic risk factors and brain health. Whereas the causality needs to be determined, the observed patterns of body-brain relationships provide a foundation for understanding the underlying mechanisms linking psychiatric disorders with obesity and cardiovascular disease, with potential for the development of new prevention strategies.
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| 22. |
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| 23. |
- Kaufmann, Tobias, et al.
(författare)
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Common brain disorders are associated with heritable patterns of apparent aging of the brain
- 2019
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Ingår i: Nature Neuroscience. - : Nature Publishing Group. - 1097-6256 .- 1546-1726. ; 22:10, s. 1617-
-
Tidskriftsartikel (refereegranskat)abstract
- Common risk factors for psychiatric and other brain disorders are likely to converge on biological pathways influencing the development and maintenance of brain structure and function across life. Using structural MRI data from 45,615 individuals aged 3-96 years, we demonstrate distinct patterns of apparent brain aging in several brain disorders and reveal genetic pleiotropy between apparent brain aging in healthy individuals and common brain disorders.
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| 24. |
- Lalani, Tahaniyat, et al.
(författare)
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Propionibacterium endocarditis: a case series from the International Collaboration on Endocarditis Merged Database and Prospective Cohort Study.
- 2007
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Ingår i: Scandinavian journal of infectious diseases. - : Informa UK Limited. - 0036-5548 .- 1651-1980. ; 39:10, s. 840-8
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Tidskriftsartikel (refereegranskat)abstract
- Propionibacterium species are occasionally associated with serious systemic infections such as infective endocarditis. In this study, we examined the clinical features, complications and outcome of 15 patients with Propionibacterium endocarditis using the International Collaboration on Endocarditis Merged Database (ICE-MD) and Prospective Cohort Study (ICE-PCS), and compared the results to 28 cases previously reported in the literature. In the ICE database, 11 of 15 patients were male with a mean age of 52 y. Prosthetic valve endocarditis occurred in 13 of 15 cases and 3 patients had a history of congenital heart disease. Clinical findings included valvular vegetations (9 patients), cardiac abscesses (3 patients), congestive heart failure (2 patients), and central nervous system emboli (2 patients). Most patients were treated with beta-lactam antibiotics alone or in combination for 4 to 6 weeks. 10 of the 15 patients underwent valve replacement surgery and 2 patients died. Similar findings were noted on review of the literature. The results of this paper suggest that risk factors for Propionibacterium endocarditis include male gender, presence of prosthetic valves and congenital heart disease. The clinical course is characterized by complications such as valvular dehiscence, cardiac abscesses and congestive heart failure. Treatment may require a combination of medical and surgical therapy.
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| 25. |
- Lindberg, Oskar, et al.
(författare)
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Day-ahead probabilistic forecasting at a co-located wind and solar power park in Sweden : Trading and forecast verification
- 2023
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Ingår i: Advances in Applied Energy. - : Elsevier. - 2666-7924. ; 9
-
Tidskriftsartikel (refereegranskat)abstract
- This paper presents a first step in the field of probabilistic forecasting of co-located wind and photovoltaic (PV) parks. The effect of aggregation is analyzed with respect to forecast accuracy and value at a co-located park in Sweden using roughly three years of data. We use a fixed modelling framework where we post-process numerical weather predictions to calibrated probabilistic production forecasts, which is a prerequisite when placing optimal bids in the day-ahead market. The results show that aggregation improves forecast accuracy in terms of continuous ranked probability score, interval score and quantile score when compared to wind or PV power forecasts alone. The optimal aggregation ratio is found to be 50%–60% wind power and the remainder PV power. This is explained by the aggregated time series being smoother, which improves the calibration and produces sharper predictive distributions, especially during periods of high variability in both resources, i.e., most prominently in the summer, spring and fall. Furthermore, the daily variability of wind and PV power generation was found to be anti-correlated which proved to be beneficial when forecasting the aggregated time series. Finally, we show that probabilistic forecasts of co-located production improve trading in the day-ahead market, where the more accurate and sharper forecasts reduce balancing costs. In conclusion, the study indicates that co-locating wind and PV power parks can improve probabilistic forecasts which, furthermore, carry over to electricity market trading. The results from the study should be generally applicable to other co-located parks in similar climates.
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| 26. |
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| 27. |
- Munkhammar, Joakim, 1982-, et al.
(författare)
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Probabilistic forecasting of high-resolution clear-sky index time-series using a Markov-chain mixture distribution model
- 2019
-
Ingår i: Solar Energy. - : Elsevier BV. - 0038-092X .- 1471-1257. ; 184, s. 688-695
-
Tidskriftsartikel (refereegranskat)abstract
- This study presents a Markov-chain mixture (MCM) distribution model for forecasting the clear-sky index-normalized global horizontal irradiance. The model is presented in general, but applied to, and tested or minute resolution clear-sky index data for the two different climatic regions of Norrkoping, Sweden, and Hawaii USA. Model robustness is evaluated based on a cross-validation procedure and on that basis a reference con figuration of parameter settings for evaluating the model performance is obtained. Simulation results ar compared with persistence ensemble (PeEn) and quantile regression (QR) model simulations for both data set and for D = 1,...,5 steps ahead forecasting scenarios. The results are evaluated by a set of probabilistic fore casting metrics: reliability mean absolute error (reliability MAE), prediction interval normalized average widti (PINAW), continuous ranked probability score (CRPS) and continuous ranked probability skill score (skill). Botl in terms of reliability MAE and CRPS, the MCM model outperforms PeEn for all simulated scenarios. In terms c reliability MAE, the QR model outperforms the MCM model for most simulated scenarios. However, in terms c mean CRPS, the MCM model outperforms the QR model in most simulated scenarios. A point forecasting esti mate is also provided. The MCM model is concluded to be a computationally inexpensive, accurate and pars meter insensitive probabilistic model. Based on this, it is suggested as a candidate benchmark model in prop abilistic forecasting, in particular for solar irradiance forecasting. For applicability, a Python script of the MCA model is available as SheperoMah/MCM-distribution-forecasting at GitHub.
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| 28. |
- Munkhammar, Joakim, 1982-, et al.
(författare)
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Probabilistic forecasting of the clear-sky index using Markov-chain mixture distribution and copula models
- 2019
-
Ingår i: 2019 Ieee 46Th Photovoltaic Specialists Conference (PVSC). - New York : IEEE. - 9781728104942 ; , s. 2428-2433
-
Konferensbidrag (refereegranskat)abstract
- Two probabilistic forecasting models for the clear-sky index, based on the Markov-chain mixture distribution (MCM) and copula clear-sky index generators, are presented and evaluated. In terms of performance, these models are compared with two benchmark models: a Quantile Regression (QR) model and the Persistence Ensemble (PeEn). The models are tested on minute resolution clear-sky index data, which was estimated from irradiance data for two different climatic regions: Hawaii, USA and Norrkoping, Sweden. Results show that the copula model generally outperforms the PeEn, while the MCM and QR models are superior in all tested aspects. Comparing MCM and QR reliability, the QR is superior, while the MCM is superior in mean CRPS and skill score. The MCM model is proposed as a potential benchmark for probabilistic solar forecasting. The MCM model is available in Python as SheperoMah/MCM-distribution-forecasting at GitHub.
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| 29. |
- Munkhammar, Joakim, 1982-, et al.
(författare)
-
Very short term load forecasting of residential electricity consumption using the Markov-chain mixture distribution (MCM) model
- 2021
-
Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 282
-
Tidskriftsartikel (refereegranskat)abstract
- This study utilizes the Markov-chain mixture distribution model (MCM) for very short term load forecasting of residential electricity consumption. The model is used to forecast one step ahead half hour resolution residential electricity consumption data from Australia. The results are compared with Quantile Regression (QR) and Persistence Ensemble (PeEn) as advanced and simple benchmark models. The results were compared in terms of reliability, reliability mean absolute error (rMAE), prediction interval normalized average width (PINAW) and normalized continuous ranked probability score (nCRPS). For 10 steps conditioning for QR and PeEn, the MCM results were on par with QR, and superior to PeEn. As a sensitivity analysis, simulations were performed where the number of data points for conditioning QR and PeEn was varied and compared to the MCM output, which is based on only one data point for conditioning. It was shown that in terms of nCRPS and rMAE the QR results converged towards the MCM results for lower number of conditioning points included in QR. The nCRPS of PeEn never reached the superior MCM and QR results, but in rMAE, for number of conditioning points above 24, PeEn was the most reliable. Based on the sparse complexity design of MCM, high computational speed and competitive performance, it is suggested as a candidate for benchmark model in probabilistic forecasting of electricity consumption.
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| 30. |
- Schindler, Louise S., et al.
(författare)
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Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women
- 2022
-
Ingår i: NeuroImage. - : ELSEVIER SCI LTD. - 2213-1582. ; 36
-
Tidskriftsartikel (refereegranskat)abstract
- The menopause transition involves changes in oestrogens and adipose tissue distribution, which may influence female brain health post-menopause. Although increased central fat accumulation is linked to risk of cardiometabolic diseases, adipose tissue also serves as the primary biosynthesis site of oestrogens post-menopause. It is unclear whether different types of adipose tissue play diverging roles in female brain health post-menopause, and whether this depends on lifetime oestrogen exposure, which can have lasting effects on the brain and body even after menopause. Using the UK Biobank sample, we investigated associations between brain characteristics and visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) in 10,251 post-menopausal females, and assessed whether the relationships varied depending on length of reproductive span (age at menarche to age at menopause). To parse the effects of common genetic variation, we computed polygenic scores for reproductive span. The results showed that higher VAT and ASAT were both associated with higher grey and white matter brain age, and greater white matter hyperintensity load. The associations varied positively with reproductive span, indicating more prominent associations between adipose tissue and brain measures in females with a longer reproductive span. The effects were in general small, but could not be fully explained by genetic variation or relevant confounders. Our findings indicate that associations between abdominal adipose tissue and brain health post-menopause may partly depend on individual differences in cumulative oestrogen exposure during reproductive years, emphasising the complexity of neural and endocrine ageing processes in females.
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| 31. |
- Shepero, Mahmoud, 1992-, et al.
(författare)
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Residential probabilistic load forecasting : A method using Gaussian process designed for electric load data
- 2018
-
Ingår i: Applied Energy. - : Elsevier BV. - 0306-2619 .- 1872-9118. ; 218, s. 159-172
-
Tidskriftsartikel (refereegranskat)abstract
- Probabilistic load forecasting (PLF) is of important value to grid operators, retail companies, demand response aggregators, customers, and electricity market bidders. Gaussian processes (GPs) appear to be one of the promising methods for providing probabilistic forecasts. In this paper, the log-normal process (LP) is newly introduced and compared to the conventional GP. The LP is especially designed for positive data like residential load forecasting—little regard was taken to address this issue previously. In this work, probabilisitic and deterministic error metrics were evaluated for the two methods. In addition, several kernels were compared. Each kernel encodes a different relationship between inputs. The results showed that the LP produced sharper forecasts compared with the conventional GP. Both methods produced comparable results to existing PLF methods in the literature. The LP could achieve as good mean absolute error (MAE), root mean square error (RMSE), prediction interval normalized average width (PINAW) and prediction interval coverage probability (PICP) as 2.4%, 4.5%, 13%, 82%, respectively evaluated on the normalized load data.
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| 32. |
- Tønnesen, Siren, et al.
(författare)
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Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder : A Multisample Diffusion Tensor Imaging Study
- 2020
-
Ingår i: Biological Psychiatry. - : Elsevier BV. - 2451-9022 .- 2451-9030. ; 5:12, s. 1095-1103
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts.METHODS: We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18-94 years of age) and applied the models to the test sets including 648 patients with SZ (18-66 years of age), 185 patients with BD (18-64 years of age), and 990 HC subjects (17-68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results.RESULTS: Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen's d = -0.29) and patients with BD (Cohen's d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy-based models showed larger group differences than the models based on other DTI-derived metrics.CONCLUSIONS: Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.
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| 33. |
- van der Meer, Dennis
(författare)
-
A benchmark for multivariate probabilistic solar irradiance forecasts
- 2021
-
Ingår i: Solar Energy. - : Elsevier. - 0038-092X .- 1471-1257. ; 225, s. 286-296
-
Tidskriftsartikel (refereegranskat)abstract
- It is well-known that decision-making processes benefit from the inclusion of uncertainty. Such optimization problems typically extend over a control horizon and could span multiple locations or regions. In addition to uncertainty, these optimization problems require as input a trajectory of scalar values that exhibits the correct spatial and temporal dependencies. Probabilistic forecasts quantify the uncertainty by means of quantiles, predictive distributions or ensembles for a forecast horizon and a site or a region separately, and therefore generally lack spatial and temporal dependencies. One solution is to use a copula to model the spatial or temporal dependencies, which, in combination with the probabilistic forecasts, can be used to issue correlated trajectory forecasts. However, there is currently no benchmark model available to compare multivariate probabilistic solar forecasts with. This paper proposes a multivariate probabilistic ensemble (MuPEn) benchmark model and shows that it generalizes the complete-history persistence ensemble (CH-PeEn) to the multivariate case. The proposed benchmark model requires a forecast issue time and a forecast horizon to construct a multivariate empirical distribution of historical clear-sky index measurements from which a multivariate ensemble forecast can be sampled. Similar to CH-PeEn, the proposed benchmark model generates forecasts that are generally calibrated and consistent in terms of energy score and variogram score.
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| 34. |
- van der Meer, Dennis, et al.
(författare)
-
A comparison of strategies for net demand forecasting in case of PV power production and electricity consumption
- 2017
-
Konferensbidrag (refereegranskat)abstract
- This paper aims to investigate the relative difference in accuracy between forecasting net demand, i.e., electricity con- sumption less the photovoltaic (PV) power production, directly and indirectly, where the latter implies forecasting consumption and production separately before subtraction. Depending on the variability and penetration of PV power production, variability of the net demand time series is likely to increase as well, which may influence accuracy of the forecast. The well-known AutoRegressive Integrated Moving Average (ARIMA) model is employed to forecast the univariate time series. We show that the direct strategy leads to a forecast with higher accuracy. Moreover, the difference in accuracy between the strategies appears to increase with lead time.
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| 35. |
- van der Meer, Dennis, et al.
(författare)
-
An alternative optimal strategy for stochastic model predictive control of a residential battery energy management system with solar photovoltaic
- 2021
-
Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 283
-
Tidskriftsartikel (refereegranskat)abstract
- Scenario-based stochastic model predictive control traditionally considers the optimal strategy to be the expectation of the optimal strategies across all scenarios. However, while the stochastic problem involving uncertainties can be substantiated by a large number of scenarios, the expectation of the respective optimal control strategies derived from all scenarios as the optimal control strategy to the problem is challenging to justify. We therefore propose a different approach in which we artfully have the optimization program find the common optimal strategy across all scenarios for the first prediction step at each sample time, which, if it exists, yields the true optimal strategy with greater confidence. We demonstrate the efficacy of the proposed formulation through a case study of a research villa in Borås, Sweden, that is equipped with a battery and a photovoltaic system. We compute a covariance matrix that contains time-dependent information of the data and use it to sample autocorrelated scenarios from the probabilistic forecasts that serve as the uncertain input to the energy management system. We justify the credibility of the optimal solution derived from the proposed formulation with compelling reasoning and quantitative results such as improved self-consumption of photovoltaic power.
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| 36. |
- van der Meer, Dennis, et al.
(författare)
-
Clear-sky index space-time trajectories from probabilistic solar forecasts : Comparing promising copulas
- 2020
-
Ingår i: Journal of Renewable and Sustainable Energy. - : AMER INST PHYSICS. - 1941-7012. ; 12:2
-
Tidskriftsartikel (refereegranskat)abstract
- Short-term probabilistic solar forecasts are an important tool in decision-making processes in which uncertainty plays a non-negligible role. Purely statistical models that produce temporal or spatiotemporal probabilistic solar forecasts are generally trained individually, and the combined forecasts therefore lack the temporal or spatiotemporal correlation present in the data. To recover the spatiotemporal dependence structure, a copula can be employed, which constructs a multivariate distribution from which spatially and temporally correlated uniform random numbers can be sampled, which in turn can be used to generate the so-called space-time trajectories via the inverse probability integral transform. In this study, we employ the recently introduced ultra-fast preselection algorithm to leverage the spatiotemporal information present in a pyranometer network and compare its accuracy to that of quantile regression forecasts that only consider temporal information. We show that the preselection algorithm improves both the calibration and sharpness of the predictive distributions. Furthermore, we employ four copulas, i.e., (1) Gaussian, (2) Student-t, (3) Clayton, and (4) empirical, to generate space-time trajectories. The results highlight the necessity to rigorously assess the calibration of the space-time trajectories and the correct modeling of the spatiotemporal dependence structure, which we show through techniques introduced in atmospheric sciences. The code used to generate the results in this study can be found at https://github.com/DWvanderMeer/SpaceTimeTrajectories.
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| 37. |
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| 38. |
- van der Meer, Dennis, et al.
(författare)
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Data-Enabled Reactive Power Control of Distributed Energy Resources via a Copula Estimation of Distribution Algorithm
- 2022
-
Ingår i: 2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665412117
-
Konferensbidrag (refereegranskat)abstract
- The increase in the number of distributed energy resources (DERs) in the low-voltage (LV) grid causes reverse active power flow, which induces voltage regulation issues across the feeder. We employ the copula estimation of distribution algorithm (copula EDA) that optimally controls the reactive power of DERs to minimize voltage deviations. EDAs iteratively learn from data and sample an explicit probability distribution that models the dependencies between variables, allowing for a more effective exploration of the optimal solution space with fewer iterations. A copula offers additional flexibility, since the dependence structure between the decision variables and the marginal distributions can be modeled independently. The effectiveness of the proposed method is illustrated on a modified IEEE 123 node test feeder with 10 smart photovoltaic inverters. The results show that the proposed method achieves improved voltage profiles and offers many opportunities for further adaptability.
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| 39. |
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| 40. |
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| 41. |
- van der Meer, Dennis, et al.
(författare)
-
Predicting hosting capacity of photovoltaic power production in low-voltage grids using regressive techniques
- 2017
-
Konferensbidrag (refereegranskat)abstract
- In this study we predict the hosting capacity (HC) of photovoltaic (PV) power of low-voltage (LV) grids utilizing explanatory variables that are straightforward for stakeholders to determine. The motivation of this study is to avoid the necessity of simulating electricity grids using power flow analysis, which are generally time consuming in terms of both coding and solving. In order to achieve this, we utilize extensive power flow simulations performed on two medium-voltage (MV) grids in Herrljunga, Sweden, and extract explanatory variables that show high correlation with HC. Furthermore, we employ multiple linear regression (MLR), gradient boosting (GB) and Gaussian process (GP) to predict HC. The results reveal that HC can be predicted with reasonable accuracy, achieving MAE between 12.2 kW and 14.0 kW, and RMSE between 15.7 kW and 17.4 kW, and can therefore guide stakeholders by providing an accurate first estimate.
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| 42. |
- van der Meer, Dennis, et al.
(författare)
-
Probabilistic clear-sky index forecasts using Gaussian process ensembles
- 2018
-
Ingår i: 2018 IEEE 7th World Conference On Photovoltaic Energy Conversion (WCPEC). - : IEEE. - 9781538685297 ; , s. 2724-2729
-
Konferensbidrag (refereegranskat)abstract
- In this paper, we investigate the performance of ensembles of Gaussian processes (GPs) to provide more accurate probabilistic forecasts of the clear-sky index (CSI), based on data from a network of pyranometers on Hawaii. This idea follows from the multiple-state model of the CSI in which its probability density can he represented as a combination of Gaussian densities, and the well-documented advantage of ensembles of prediction models. More specifically, we employ a Gaussian mixture model (GMM) and convolution to produce ensembles of GPs, and show that the GMM ensemble outperforms the individual and convoluted GP models, especially by improving the lower limit of the skill score.
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| 43. |
- van der Meer, Dennis, et al.
(författare)
-
Probabilistic forecasting of solar power, electricity consumption and net load : Investigating the effect of seasons, aggregation and penetration on prediction intervals
- 2018
-
Ingår i: Solar Energy. - : Elsevier BV. - 0038-092X .- 1471-1257. ; 171, s. 397-413
-
Tidskriftsartikel (refereegranskat)abstract
- This paper presents a study into the effect of aggregation of customers and an increasing share of photovoltaic (PV) power in the net load on prediction intervals (PIs) of probabilistic forecasting methods applied to dis- tribution grid customers during winter and spring. These seasons are shown to represent challenging cases due to the increased variability of electricity consumption during winter and the increased variability in PV power production during spring. We employ a dynamic Gaussian process (GP) and quantile regression (QR) to produce probabilistic forecasts on data from 300 de-identified customers in the metropolitan area of Sydney, Australia. In case of the dynamic GP, we also optimize the training window width and show that it produces sharp and reliable PIs with a training set of up to 3 weeks. In case of aggregation, the results indicate that the aggregation of a modest number of PV systems improves both the sharpness and the reliability of PIs due to the smoothing effect, and that this positive effect propagates into the net load forecasts, especially for low levels of aggregation. Finally, we show that increasing the share of PV power in the net load actually increases the sharpness and reliability of PIs for aggregations of 30 and 210 customers, most likely due to the added benefit of the smoothing effect.
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| 44. |
- van der Meer, Dennis, et al.
(författare)
-
Review on probabilistic forecasting of photovoltaic power production and electricity consumption
- 2018
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Ingår i: Renewable & sustainable energy reviews. - : Elsevier BV. - 1364-0321 .- 1879-0690. ; , s. 1484-1512
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Forskningsöversikt (refereegranskat)abstract
- Accurate forecasting simultaneously becomes more important and more challenging due to the increasing penetration of photovoltaic (PV) systems in the built environment on the one hand, and the increasing stochastic nature of electricity consumption, e.g., through electric vehicles (EVs), on the other hand. Until recently, research has mainly focused on deterministic forecasting. However, such forecasts convey little information about the possible future state of a system and since a forecast is inherently erroneous, it is important to quantify this error. This paper therefore focuses on the recent advances in the area of probabilistic forecasting of solar power (PSPF) and load forecasting (PLF). The goal of a probabilistic forecast is to provide either a complete predictive density of the future state or to predict that the future state of a system will fall in an interval, defined by a confidence level. The aim of this paper is to analyze the state of the art and assess the different approaches in terms of their performance, but also to what extent these approaches can be generalized so that they not only perform best on the data set for which they were designed, but also on other data sets or different case studies. In addition, growing interest in net demand forecasting, i.e., demand less generation, is another important motivation to combine PSPF and PLF into one review paper and assess compatibility. One important finding is that there is no single preferred model that can be applied to any circumstance. In fact, a study has shown that the same model, with adapted parameters, applied to different case studies performed well but did not excel, when compared to models that were optimized for the specific task. Furthermore, there is need for standardization, in particular in terms of filtering night time data, normalizing results and performance metrics.
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| 45. |
- van der Meer, Dennis, 1988-
(författare)
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Spatio-temporal forecasting and optimization for integration of solar energy in urban energy systems
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The increasing penetration of non-dispatchable renewable energy sources such as photovoltaic (PV) systems in the electricity generating mix poses challenges to the operational performance of the power system. On the demand side, advanced schemes that increase the flexibility of customer loads and the increase in electrification are set to noticeably alter electricity demand. Moreover, a rooftop-mounted PV system alters the electricity demand of the building it is connected to because the generated electricity first serves the electrical loads of the building, thereby affecting the so-called net load that the electricity grid experiences. This thesis studies solutions that enhance the integration of dispersed solar PV power into the power system, with a particular focus on probabilistic and multivariate forecasts and a control framework based on such forecasts. In addition, the thesis evaluates voltage control by means of reactive power control of solar PV inverters. Probabilistic solar, load and net load forecasts are generated using static and dynamic forecast models, where the latter result in approximately 99% less computation time and improved calibration and sharpness but lower forecast resolution. The dynamic forecast models are subsequently used to study the impact of spatial aggregation of customers on the predictive densities, which results in improved calibration and sharpness. Interestingly, the positive effects are already noticeable when aggregating a few customers and this can lead to improved decision-making on the community level. Multivariate forecasts in the form of time and space-time trajectories are also studied, where the multivariate distribution is represented by a copula. Specifically, it is shown that an empirical copula is particularly suitable for high-dimensional spatio-temporal forecasts whereas a Gaussian copula is well suited for temporal forecasts with a large forecast horizon. Furthermore, the thesis develops an augmented version of the scenario-based stochastic model predictive control algorithm that implements the global optimal control action---if it exists---rather than the expectation of independent optimal control actions, which manages forecast errors more effectively. Finally, a population based search method is applied to reactive power control that is able to explicitly and independently model the spatial and temporal relationship between dispersed solar PV inverters, resulting in an improved voltage profile with a smaller population compared to benchmarks. In summary, the thesis demonstrates that forecasts can be improved using several methods, e.g., by spatially aggregating customers, by combining PV power generation and electricity use, by preselecting informative predictors or by postprocessing the forecasts. In turn, the improved accuracy of the forecasts can increase their value in applications such as optimal control problems, that can improve solar PV integration in urban energy systems.
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| 46. |
- van der Meer, Dennis
(författare)
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Spatio-temporal probabilistic forecasting of solar power, electricity consumption and net load
- 2018
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The increasing penetration of renewable energy sources into the electricity generating mix poses challenges to the operational performance of the power system. Similarly, the push for energy efficiency and demand response—i.e., when electricity consumers are encouraged to alter their demand depending by means of a price signal—introduces variability on the consumption side as well.Forecasting is generally viewed as a cost-efficient method to mitigate the adverse effects of the aforementioned energy transition because it enables a grid operator to reduce the operational risk by, e.g., unit-commitment or curtailment. However, deterministic—or point—forecasting is currently still the norm.This thesis focuses on probabilistic forecasting, a method with which the uncertainty ac- companying the forecast is expressed by means of a probability distribution. In this framework, the thesis contributes to the current state-of-the-art by investigating properties of probabilistic forecasts of PV power production, electricity consumption and net load at the residential and distribution level of the electricity grid.The thesis starts with an introduction to probabilistic forecasting in general and two models in specific: Gaussian processes and quantile regression. The former model has been used to produce probabilistic forecasts of PV power production, electricity consumption and net load of individual residential buildings—particularly challenging due to the stochasticity involved— but important for home energy management systems and potential peer-to-peer energy trading. Furthermore, both models have been utilized to investigate what effects spatial aggregation and increasing penetration have on the predictive distribution. The results indicated that only 20- 25 customers—out of a data set containing 300 customers—need to be aggregated in order to improve the reliability of the probabilistic forecasts. Finally, this thesis explores the potential of Gaussian process ensembles, which is an effective way to improve the accuracy of the forecasts.
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| 47. |
- van der Meer, Dennis, et al.
(författare)
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The link between liver fat and cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body composition
- 2022
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Ingår i: Communications Biology. - : NATURE PORTFOLIO. - 2399-3642. ; 5:1
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Tidskriftsartikel (refereegranskat)abstract
- A GWAS study of European individuals uncovers genetic associations between whole-body MRI derived measures and cardiometabolic diseases and highlights the key role of liver fat in cardiometabolic health. Obesity and associated morbidities, metabolic associated fatty liver disease (MAFLD) included, constitute some of the largest public health threats worldwide. Body composition and related risk factors are known to be heritable and identification of their genetic determinants may aid in the development of better prevention and treatment strategies. Recently, large-scale whole-body MRI data has become available, providing more specific measures of body composition than anthropometrics such as body mass index. Here, we aimed to elucidate the genetic architecture of body composition, by conducting genome-wide association studies (GWAS) of these MRI-derived measures. We ran both univariate and multivariate GWAS on fourteen MRI-derived measurements of adipose and muscle tissue distribution, derived from scans from 33,588 White European UK Biobank participants (mean age of 64.5 years, 51.4% female). Through multivariate analysis, we discovered 100 loci with distributed effects across the body composition measures and 241 significant genes primarily involved in immune system functioning. Liver fat stood out, with a highly discoverable and oligogenic architecture and the strongest genetic associations. Comparison with 21 common cardiometabolic traits revealed both shared and specific genetic influences, with higher mean heritability for the MRI measures (h(2 )= .25 vs. .13, p = 1.8x10(-7)). We found substantial genetic correlations between the body composition measures and a range of cardiometabolic diseases, with the strongest correlation between liver fat and type 2 diabetes (r(g )= .49, p = 2.7x10(-22)). These findings show that MRI-derived body composition measures complement conventional body anthropometrics and other biomarkers of cardiometabolic health, highlighting the central role of liver fat, and improving our knowledge of the genetic architecture of body composition and related diseases.
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| 48. |
- van der Meer, Dennis, et al.
(författare)
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The role of liver fat in cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body composition
- 2022
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Background & AimsObesity and associated morbidities, metabolic associated liver disease (MAFLD) included, constitute some of the largest public health threats worldwide. Body composition and related risk factors are known to be heritable and identification of their genetic determinants may aid in the development of better prevention and treatment strategies. Recently, large-scale whole-body MRI data has become available, providing more specific measures of body composition than anthropometrics such as body mass index. Here, we aimed to elucidate the genetic architecture of body composition, by conducting the first genome-wide association study (GWAS) of these MRI-derived measures.MethodsWe ran both univariate and multivariate GWAS on fourteen MRI-derived measurements of adipose and muscle tissue distribution, derived from scans from 34,036 White European UK Biobank participants (mean age of 64.5 years, 51.5% female).ResultsThrough multivariate analysis, we discovered 108 loci with distributed effects across the body composition measures and 256 significant genes primarily involved in immune system functioning. Liver fat stood out, with a highly discoverable and oligogenic architecture and the strongest genetic associations. Comparison with 21 common cardiometabolic traits revealed both shared and specific genetic influences, with higher mean heritability for the MRI measures (h2=.25 vs. .16, p=1.4×10−6). We found substantial genetic correlations between the body composition measures and a range of cardiometabolic diseases, with the strongest correlation between liver fat and type 2 diabetes (rg=.48, p=1.6×10−22).ConclusionsThese findings show that MRI-derived body composition measures complement conventional body anthropometrics and other biomarkers of cardiometabolic health, highlighting the central role of liver fat, and improving our knowledge of the genetic architecture of body composition and related diseases.
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| 49. |
- van der Meer, Dennis W., et al.
(författare)
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Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes
- 2018
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Ingår i: Applied Energy. - : Elsevier BV. - 0306-2619 .- 1872-9118. ; 213, s. 195-207
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a study into the utilization of Gaussian Processes (GPs) for probabilistic forecasting of residential electricity consumption, photovoltaic (PV) power generation and net demand of a single household. The covariance function that encodes prior belief on the general shape of the time series plays a vital role in the performance of GPs and a common choice is the squared exponential (SE), although it has been argued that the SE is likely suboptimal for physical processes. Therefore, we thoroughly test various (combinations of) covariance functions. Furthermore, in order bypass the substantial learning and inference time accompanied with GPs, we investigate the potential of dynamically updating the hyperparameters using a moving training window and assess the consequences on predictive accuracy. We show that the dynamic GP produces sharper prediction intervals (PIs) than the static GP with significant lower computational burden, but at the cost of the ability to capture sharp peaks. In addition, we examine the difference in accuracy between a direct and indirect forecasting strategy in case of net demand forecasting and show that the latter is prone to producing wider PIs with higher coverage probability.
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| 50. |
- Yang, Dazhi, et al.
(författare)
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Post-processing in solar forecasting : Ten overarching thinking tools
- 2021
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Ingår i: Renewable & sustainable energy reviews. - : Elsevier. - 1364-0321 .- 1879-0690. ; 140
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Tidskriftsartikel (refereegranskat)abstract
- Forecasts are always wrong, otherwise, they are merely deterministic calculations. Besides leveraging advanced forecasting methods, post-processing has become a standard practice for solar forecasters to improve the initial forecasts. In this review, the post-processing task is divided into four categories: (1) deterministic-todeterministic (D2D) post-processing, (2) probabilistic-to-deterministic (P2D) post-processing, (3) deterministic-to-probabilistic (D2P) post-processing, and (4) probabilistic-to-probabilistic (P2P) post-processing. Additionally, a total of ten overarching thinking tools, namely, (1) regression (D2D), (2) filtering (D2D), (3) resolution change (D2D), (4) summarizing predictive distribution (P2D), (5) combining deterministic forecasts (P2D), (6) analog ensemble (D2P), (7) method of dressing (D2P), (8) probabilistic regression (D2P), (9) calibrating ensemble forecasts (P2P), and (10) combining probabilistic forecasts (P2P), are proposed. These thinking tools can be thought of as the "style" or "mechanism" of post-processing. In that, the utilization of thinking tools circumvents the common pitfalls of classifying the literature by methods (e.g., statistics, machine-learning, or numerical weather prediction), which often leads to a "who used what method" type of roster review that is clearly ineffective, non-exhaustive, and dull. When myriads of post-processing methods are mapped to countable few thinking tools, it allows solar forecasters to enumerate the styles of adjustment that could be performed on a set of initial forecasts, which makes a post-processing task clearly goal-driven. Besides the thinking tools, this paper also emphasizes on the value of post-processing, and provides an outlook for future research. Although this paper is revolved around solar, the materials herein discussed can also be applied to wind and other forecasting areas.
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