| 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
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Ingår i: Applied Energy. - : Elsevier BV. - 0306-2619 .- 1872-9118. ; 218, s. 159-172
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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
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Ingår i: Biological Psychiatry. - : Elsevier BV. - 2451-9022 .- 2451-9030. ; 5:12, s. 1095-1103
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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)
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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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| 34. |
- van der Meer, Dennis, et al.
(författare)
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A comparison of strategies for net demand forecasting in case of PV power production and electricity consumption
- 2017
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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)
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An alternative optimal strategy for stochastic model predictive control of a residential battery energy management system with solar photovoltaic
- 2021
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Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 283
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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)
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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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| 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
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Ingår i: 2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665412117
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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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