Version 1
: Received: 16 May 2024 / Approved: 17 May 2024 / Online: 17 May 2024 (11:36:14 CEST)
How to cite:
Richter, L.; Lenk, S.; Bretschneider, P. Advancing Electric Load Forecasting: Leveraging Federated Learning for Distributed, Non-Stationary, and Discontinuous Time Series. Preprints2024, 2024051157. https://doi.org/10.20944/preprints202405.1157.v1
Richter, L.; Lenk, S.; Bretschneider, P. Advancing Electric Load Forecasting: Leveraging Federated Learning for Distributed, Non-Stationary, and Discontinuous Time Series. Preprints 2024, 2024051157. https://doi.org/10.20944/preprints202405.1157.v1
Richter, L.; Lenk, S.; Bretschneider, P. Advancing Electric Load Forecasting: Leveraging Federated Learning for Distributed, Non-Stationary, and Discontinuous Time Series. Preprints2024, 2024051157. https://doi.org/10.20944/preprints202405.1157.v1
APA Style
Richter, L., Lenk, S., & Bretschneider, P. (2024). Advancing Electric Load Forecasting: Leveraging Federated Learning for Distributed, Non-Stationary, and Discontinuous Time Series. Preprints. https://doi.org/10.20944/preprints202405.1157.v1
Chicago/Turabian Style
Richter, L., Steve Lenk and Peter Bretschneider. 2024 "Advancing Electric Load Forecasting: Leveraging Federated Learning for Distributed, Non-Stationary, and Discontinuous Time Series" Preprints. https://doi.org/10.20944/preprints202405.1157.v1
Abstract
This paper addresses the evolving landscape of electricity markets in Europe, with a focus on the integration of Renewable Energy Communities as introduced by the Renewable Energy Directive 2018/2001. Residents within a postal code area are highly incentivized to join a community, which enables them to exchange energy among themselves at lower procurement costs. Thereby, energy management systems optimize the operation of respective energy systems, with electrical load forecasting playing a key role. Given that prosumers may switch between communities on a daily basis, electricity demands within these groups will vary, leading to data that is non-stationary, discontinuous as well as non-identical and independently distributed. To encounter this issue, we propose a sophisticated forecasting model that applies federated learning, using informations from various distributed communities to learn domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we ultimately evaluate their effectiveness. The findings suggest that our proposed framework is capable of effectively forecast non-stationary and discontinuous time series and that it can be applied to new, unseen data through the integration of knowledge from multiple sources.
Keywords
Federated Learning; Load Forecasting; Non-Stationary And Discontinous Time Series; Renewable Energy Community; Dynamic Portfolio
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
