Affiliations: [a] Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Zografou, Greece | [b] HOLISTIC S.A., Athens, Greece | [c] Coopérnico, Lisbon, Portugal | [d] Department of Digital Systems, University of Piraeus, Piraeus, Greece
Correspondence:
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Corresponding author: Nikos Dimitropoulos, Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Zografou, Greece. E-mail: ndimitropoulos@epu.ntua.gr.
Abstract: Energy communities can support the energy transition, by engaging citizens through collective energy actions and generate positive economic, social and environmental outcomes. Renewable Energy Sources (RES) are gaining increasing share in the electricity mix as the economy decarbonises, with Photovoltaic (PV) plants to becoming more efficient and affordable. By incorporating Artificial Intelligence (AI) techniques, innovative applications can be developed to provide added value to energy communities. In this context, the scope of this paper is to compare Machine Learning (ML) and Deep Learning (DL) algorithms for the prediction of short-term production in a solar plant under an energy cooperative operation. Three different cases are considered, based on the data used as inputs for forecasting purposes. Lagged inputs are used to assess the historical data needed, and the algorithms’ accuracy is tested for the next hour’s PV production forecast. The comparative analysis between the proposed algorithms demonstrates the most accurate algorithm in each case, depending on the available data. For the highest performing algorithm, its performance accuracy in further forecasting horizons (3 hours, 6 hours and 24 hours) is also tested.
Keywords: Energy forecasting, machine learning, deep learning, short-term prediction, artificial intelligence, energy communities, photovoltaic
