A Multi-Step Ensemble Approach for Energy Community Day-Ahead Net Load Point and Probabilistic Forecasting

Maria da Graça Ruano () and Antonio Ruano ()
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Maria da Graça Ruano: Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
Antonio Ruano: Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal

Energies, 2024, vol. 17, issue 3, 1-49

Abstract: The incorporation of renewable energy systems in the world energy system has been steadily increasing during the last few years. In terms of the building sector, the usual consumers are becoming increasingly prosumers, and the trend is that communities of energy, whose households share produced electricity, will increase in number in the future. Another observed tendency is that the aggregator (the entity that manages the community) trades the net community energy in public energy markets. To accomplish economically good transactions, accurate and reliable forecasts of the day-ahead net energy community must be available. These can be obtained using an ensemble of multi-step shallow artificial neural networks, with prediction intervals obtained by the covariance algorithm. Using real data obtained from a small energy community of four houses located in the southern region of Portugal, one can verify that the deterministic and probabilistic performance of the proposed approach is at least similar, typically better than using complex, deep models.

Keywords: multi-objective genetic algorithms; neural networks; forecasting models; ensemble models; prediction intervals; probabilistic forecasting; day-ahead energy markets (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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