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Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems Group)
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems Group)ORCID iD: 0000-0001-6292-0695
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.ORCID iD: 0000-0002-5601-1687
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems Group)
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2018 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 213, p. 195-207Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2018. Vol. 213, p. 195-207
National Category
Control Engineering Energy Systems
Identifiers
URN: urn:nbn:se:uu:diva-340597DOI: 10.1016/j.apenergy.2017.12.104ISI: 000425576900017OAI: oai:DiVA.org:uu-340597DiVA, id: diva2:1179402
Funder
Swedish Energy AgencyAvailable from: 2018-01-28 Created: 2018-02-01 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, Dennis W.Shepero, MahmoudSvensson, AndreasWidén, JoakimMunkhammar, Joakim

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