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Spatio-Temporal Probabilistic Forecasting of Solar Power Dennis van der Meer Spatio-temporal probabilistic forecasting of solar power, electricity consumption and net load 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. All models are wrong, but some are useful. George Edward Pelham Box List of papers This thesis is based on the following papers, which are referred to in the text by their Roman numerals. I D.W. van der Meer, J. Widén, J. Munkhammar, "Review on probabilistic forecasting of photovoltaic power production and electricity consumption", Renewable and Sustainable Energy Reviews, Vol. 81, pp. 1484-1512 (2018). II D.W. van der Meer, M. Shepero, A. Svensson, J. Widén, J. Munkhammar, "Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes", Applied Energy, Vol. 213, pp. 195-207 (2018). III D.W. van der Meer, J. Munkhammar, J. Widén, "Probabilistic forecasting of solar power, electricity consumption and net load: Investigating the effect of seasons, aggregation and penetration on prediction intervals", Solar Energy, Vol. 171, pp. 397-413 (2018). IV D.W. van der Meer, J. Munkhammar, J. Widén, "Probabilistic clear-sky index forecasts using Gaussian process ensembles", in Proceedings of the 2018 World Conference on Photovoltaic Energy Conversion (WCPEC-7) (IEEE Photovoltaic Specialist Conference (PVSC-45)), Waikoloa, Hawaii, June 9-15 (2018). ©2018 IEEE. Reprints were made with permission from the publishers. Papers not included in the thesis V D.W. van der Meer, G. R. Chandra Mouli, G. Morales-España, L. Ramirez Elizondo, P. Bauer, "Energy Management System With PV Power Forecast to Optimally Charge EVs at the Workplace", IEEE Trans- actions on Industrial Informatics, Vol. 14, pp. 311-320 (2018). VI M. Shepero, D.W. van der Meer, J. Munkhammar, J. Widén, "Resi- dential probabilistic load forecasting: A method using Gaussian process designed for electric load data", Applied Energy, Vol. 218, pp. 159-172 (2018). VII D.W. van der Meer, J. Widén, J. Munkhammar, "A comparison of strategies for net demand forecasting in case of photovoltaic power pro- duction and electricity consumption", in Proceedings of the 34th Euro- pean Photovoltaic Solar Energy Conference (EU-PVSEC), Amsterdam, The Netherlands, September 25-29 (2017). VIII D.W. van der Meer, J. Widén, J. Munkhammar, "Investigating the ef- fect of aggregation on prediction intervals in case of solar power, elec- tricity consumption and net demand forecasting", in Proceedings of the 7th Solar Integration Workshop (SIW), Berlin, Germany, October 24-25 (2017). IX D.W. van der Meer, J. Andersson, V. Bernström, J. Törnqvist, J. Widén, "Predicting hosting capacity of photovoltaic power production in low- voltage grids using regressive techniques", in Proceedings of the 7th So- lar Integration Workshop (SIW), Berlin, Germany, October 24-25 (2017). Notes on my contributions I contributed with the following in the appended papers: Paper I, I surveyed the literature and wrote the paper. Paper II, I co-developed the model, performed most of the simulations and wrote most of the paper. Paper III, I developed the models, performed the simulations and wrote the paper. Paper IV, I developed the model, performed the simulations and wrote the paper. Ethical considerations Ethics is a broad field of research with a long history. Although typically thought of as mainly applicable in studies involving people and animals, ethics plays an important role in all scientific fields, albeit in different forms. In this brief chapter, the focus lies on the justification of this licentiate thesis with regards to society, truth telling and impartiality. Ethics constitutes an important aspect of one of today’s fundamental chal- lenges: climate change. Although the majority of researchers agree that ev- idence exists for anthropogenic climate change, controversy exists regarding the extent of it [1]. It is, then, important to be objective as to the justification and purpose of research related to mitigating climate change, which concerns renewable energy in this licentiate thesis. Renewable energy sources (RESs) will play an important role in sustainable energy production but they pose challenges to the stability and reliability of the energy system due to their variable nature. It has been hypothesized and proven that the ability to ac- curately predict allows for further integration of RESs while reducing energy generation costs and mitigating greenhouse gas (GHG) emissions. In this li- centiate thesis, the aim is to advance the field of forecasting and subsequent decision making by considering spatio-temporal probabilistic forecasts, which should—at least ideally—advance the integration of RESs into the electricity generating mix. It is, however, important to point out that this licentiate thesis or RESs in general will not be sufficient to mitigate climate change and other alternatives, e.g., reforestation, carbon capture and storage (CSS) and energy efficiency, require further research efforts as well. Truth telling and impartiality are vital to sustain the credibility of science. In probabilistic forecasting, the research topic of this licentiate thesis, proper scoring rules are used that give the highest reward—in some sense—in expec- tation by reporting the true probability distribution [2]. It therefore encourages the researcher to be truthful so that he or she may continue to improve the fore- cast accuracy and maximize the reward in expectation [3]. Impartiality, on the other hand, is not defined mathematically. Consequently, it requires dedication from the researcher to remain impartial and objective. An important aspect of impartiality is conflict of interest, which is defined as the risk that secondary interests may affect the primary interest by unsound judgment or actions [4]. In this licentiate thesis, there is no conflict of interest. Contents Ethical considerations ....................................................................................... ix 1 Introduction .................................................................................................. 3 1.1 Aim of the thesis .............................................................................. 4 1.2 Overview of the thesis and the appended papers ........................... 5 2 Background .................................................................................................. 7 2.1 Why do we need to forecast? .......................................................... 7 2.1.1 Distributed generation ....................................................... 7 2.1.2 Electricity consumption 2.0 .............................................. 9 2.1.3 Net load ............................................................................ 10 2.1.4 Balancing supply and demand ........................................ 10 2.1.5 Deterministic versus probabilistic forecasting ............... 11 2.2 How can we forecast? .................................................................... 12 2.2.1 Numerical weather prediction ......................................... 13 2.2.2 Satellites ........................................................................... 14 2.2.3 Statistical machine learning ............................................ 14 2.2.4 Hybrid methods ............................................................... 15 2.3 Basic definitions ............................................................................. 15 2.4 Previous work ................................................................................
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