Short-Term Forecasting of Energy Production and Consumption Using Extreme Learning Machine: A Comprehensive MIMO based ELM Approach

• URL: http://arxiv.org/abs/2508.12764v2
• Date: Fri, 22 Aug 2025 06:16:11 GMT
• Title: Short-Term Forecasting of Energy Production and Consumption Using Extreme Learning Machine: A Comprehensive MIMO based ELM Approach
• Authors: Cyril Voyant, Milan Despotovic, Luis Garcia-Gutierrez, Mohammed Asloune, Yves-Marie Saint-Drenan, Jean-Laurent Duchaud, hjuvan Antone Faggianelli, Elena Magliaro,
• Abstract summary: A novel methodology for short-term energy forecasting using an Extreme Learning Machine ($mathtELM$) is proposed.<n>Using six years of hourly data collected in Corsica (France) from multiple energy sources, our approach predicts both individual energy outputs and total production.<n>The model maintains high accuracy up to five hours ahead, beyond which renewable energy sources become increasingly volatile.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A novel methodology for short-term energy forecasting using an Extreme Learning Machine ($\mathtt{ELM}$) is proposed. Using six years of hourly data collected in Corsica (France) from multiple energy sources (solar, wind, hydro, thermal, bioenergy, and imported electricity), our approach predicts both individual energy outputs and total production (including imports, which closely follow energy demand, modulo losses) through a Multi-Input Multi-Output ($\mathtt{MIMO}$) architecture. To address non-stationarity and seasonal variability, sliding window techniques and cyclic time encoding are incorporated, enabling dynamic adaptation to fluctuations. The $\mathtt{ELM}$ model significantly outperforms persistence-based forecasting, particularly for solar and thermal energy, achieving an $\mathtt{nRMSE}$ of $17.9\%$ and $5.1\%$, respectively, with $\mathtt{R^2} > 0.98$ (1-hour horizon). The model maintains high accuracy up to five hours ahead, beyond which renewable energy sources become increasingly volatile. While $\mathtt{MIMO}$ provides marginal gains over Single-Input Single-Output ($\mathtt{SISO}$) architectures and offers key advantages over deep learning methods such as $\mathtt{LSTM}$, it provides a closed-form solution with lower computational demands, making it well-suited for real-time applications, including online learning. Beyond predictive accuracy, the proposed methodology is adaptable to various contexts and datasets, as it can be tuned to local constraints such as resource availability, grid characteristics, and market structures.

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