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Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting
Hopf, Konstantin; Hartstang, Hannah; Staake, Thorsten Robert (2023): Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting, in: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems (e-Energy), Bamberg: Otto-Friedrich-Universität.
Faculty/Chair:
Author:
Title of the compilation:
Companion Proceedings of the 14th ACM International Conference on Future Energy Systems (e-Energy)
Conference:
e-Energy Workshop 2023 : International Workshop on Energy Data and Analytics ; Orlando, FL
Publisher Information:
Year of publication:
2023
Source/Other editions:
Hopf, Konstantin; Hartstang, Hannah; Staake, Thorsten Robert: Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting. In: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems (e-Energy) ; e-Energy Workshop 2023: International Workshop on Energy Data and Analytics ; Orlando, FL. ACM digital library S. 32–39, 2023. DOI: 10.1145/3599733.3600248
Language:
English
Licence:
Abstract:
Forecasting electricity demand plays a critical role in ensuring reliable and cost-efficient operation of the electricity supply. With the global transition to distributed renewable energy sources and the electrification of heating and transportation, accurate load forecasts become even more important. While numerous empirical studies and a handful of review articles exist, there is surprisingly little quantitative analysis of the literature, most notably none that identifies the impact of factors on forecasting performance across the entirety of empirical studies. In this article, we therefore present a Meta-Regression Analysis (MRA) that examines factors that influence the accuracy of short-term electricity load forecasts. We use data from 421 forecast models published in 59 studies. While the grid level (esp. individual vs. aggregated vs. system), the forecast granularity, and the algorithms used seem to have a significant impact on the MAPE, bibliometric data, dataset sizes, and prediction horizon show no significant effect. We found the LSTM approach and a combination of neural networks with other approaches to be the best forecasting methods. The results help practitioners and researchers to make meaningful model choices. Yet, this paper calls for further MRA in the field of load forecasting to close the blind spots in research and practice of load forecasting.
GND Keywords: ; ; ; ;
Elektrizitätsnachfrage
Kurzfristige Prognose
Prognose
Regressionsanalyse
Prognosefehler
Keywords: ; ; ;
Electricity Demand Forecast
Short-Term Forecasting
Meta Regression Analysis
Mean Absolute Percentage Error
DDC Classification:
RVK Classification:
Peer Reviewed:
Yes:
International Distribution:
Yes:
Type:
Conferenceobject
Activation date:
November 6, 2023
Permalink
https://fis.uni-bamberg.de/handle/uniba/91304