| 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. |
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| 3. |
- 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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| 4. |
- van der Meer, Dennis
(författare)
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A benchmark for multivariate probabilistic solar irradiance forecasts
- 2021
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Ingår i: Solar Energy. - : Elsevier. - 0038-092X .- 1471-1257. ; 225, s. 286-296
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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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| 5. |
- Yang, Dazhi, et al.
(författare)
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Verification of deterministic solar forecasts
- 2020
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Ingår i: Solar Energy. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0038-092X .- 1471-1257. ; 210, s. 20-37
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Tidskriftsartikel (refereegranskat)abstract
- The field of energy forecasting has attracted many researchers from different fields (e.g., meteorology, data sciences, mechanical or electrical engineering) over the last decade. Solar forecasting is a fast-growing sub-domain of energy forecasting. Despite several previous attempts, the methods and measures used for verification of deterministic (also known as single-valued or point) solar forecasts are still far from being standardized, making forecast analysis and comparison difficult. To analyze and compare solar forecasts, the well-established Murphy-Winkler framework for distribution-oriented forecast verification is recommended as a standard practice. This framework examines aspects of forecast quality, such as reliability, resolution, association, or discrimination, and analyzes the joint distribution of forecasts and observations, which contains all time-independent information relevant to verification. To verify forecasts, one can use any graphical display or mathematical/statistical measure to provide insights and summarize the aspects of forecast quality. The majority of graphical methods and accuracy measures known to solar forecasters are specific methods under this general framework. Additionally, measuring the overall skillfulness of forecasters is also of general interest. The use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended. By standardizing the accuracy measure and reference forecasting method, the RMSE skill score allows-with appropriate caveats-comparison of forecasts made using different models, across different locations and time periods.
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| 6. |
- 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
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Ingår i: Solar Energy. - : Elsevier BV. - 0038-092X .- 1471-1257. ; 184, s. 688-695
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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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| 7. |
- 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
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Ingår i: 2019 Ieee 46Th Photovoltaic Specialists Conference (PVSC). - New York : IEEE. - 9781728104942 ; , s. 2428-2433
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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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| 8. |
- van der Meer, Dennis, et al.
(författare)
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Clear-sky index space-time trajectories from probabilistic solar forecasts : Comparing promising copulas
- 2020
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Ingår i: Journal of Renewable and Sustainable Energy. - : AMER INST PHYSICS. - 1941-7012. ; 12:2
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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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| 9. |
- 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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