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Review on probabilistic forecasting of photovoltaic power production and electricity consumption
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (BEESG)
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (BEESG)ORCID iD: 0000-0003-4887-9547
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (BEESG)ORCID iD: 0000-0003-0051-4098
2018 (English)In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, p. 1484-1512Article, review/survey (Refereed) Published
Abstract [en]

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. 

Place, publisher, year, edition, pages
2018. p. 1484-1512
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:uu:diva-332709DOI: 10.1016/j.rser.2017.05.212ISI: 000417070500106OAI: oai:DiVA.org:uu-332709DiVA, id: diva2:1153867
Funder
Swedish Energy AgencyAvailable from: 2017-10-31 Created: 2017-10-31 Last updated: 2018-10-18Bibliographically approved
In thesis
1. Spatio-temporal probabilistic forecasting of solar power, electricity consumption and net load
Open this publication in new window or tab >>Spatio-temporal probabilistic forecasting of solar power, electricity consumption and net load
2018 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Uppsala: Institutionen för teknikvetenskaper, 2018. p. 61
National Category
Engineering and Technology
Identifiers
urn:nbn:se:uu:diva-363448 (URN)
Presentation
2018-11-16, 2005, Lägerhyddsvägen 1, Uppsala, 13:15 (English)
Opponent
Supervisors
Funder
Swedish Energy Agency
Available from: 2018-10-18 Created: 2018-10-18 Last updated: 2018-11-06

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van der Meer, DennisWidén, JoakimMunkhammar, Joakim

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