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A Machine Learning Approach to Forecasting Hydropower Generation

Author

Listed:
  • Sarah Di Grande

    (Department of Electrical Electronic and Computer Engineering, University of Catania, Viale A. Doria 6, 95125 Catania, Italy)

  • Mariaelena Berlotti

    (Department of Electrical Electronic and Computer Engineering, University of Catania, Viale A. Doria 6, 95125 Catania, Italy)

  • Salvatore Cavalieri

    (Department of Electrical Electronic and Computer Engineering, University of Catania, Viale A. Doria 6, 95125 Catania, Italy)

  • Roberto Gueli

    (Etna Hitech S.C.p.A., Viale Africa 31, 95129 Catania, Italy)

Abstract

In light of challenges like climate change, pollution, and depletion of fossil fuel reserves, governments and businesses prioritize renewable energy sources such as solar, wind, and hydroelectric power. Renewable energy forecasting models play a crucial role for energy market operators and prosumers, aiding in planning, decision-making, optimization of energy sales, and evaluation of investments. This study aimed to develop machine learning models for hydropower forecasting in plants integrated into Water Distribution Systems, where energy is generated from water flow used for municipal water supply. The study involved developing and comparing monthly and two-week forecasting models, utilizing both one-step-ahead and two-step-ahead forecasting methodologies, along with different missing data imputation techniques. The tested algorithms—Seasonal Autoregressive Integrated Moving Average, Random Forest, Temporal Convolutional Network, and Neural Basis Expansion Analysis for Time Series—produced varying levels of performance. The Random Forest model proved to be the most effective for monthly forecasting, while the Temporal Convolutional Network delivered the best results for two-week forecasting. Across all scenarios, the seasonal–trend decomposition using the LOESS technique emerged as the most successful for missing data imputation. The accurate predictions obtained demonstrate the effectiveness of using these models for energy planning and decision-making.

Suggested Citation

  • Sarah Di Grande & Mariaelena Berlotti & Salvatore Cavalieri & Roberto Gueli, 2024. "A Machine Learning Approach to Forecasting Hydropower Generation," Energies, MDPI, vol. 17(20), pages 1-22, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:20:p:5163-:d:1500387
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    References listed on IDEAS

    as
    1. Sooyeon Yi & G. Mathias Kondolf & Samuel Sandoval-Solis & Larry Dale, 2022. "Application of Machine Learning-based Energy Use Forecasting for Inter-basin Water Transfer Project," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5675-5694, November.
    2. Obahoundje, Salomon & Diedhiou, Arona & Dubus, Laurent & Adéchina Alamou, Eric & Amoussou, Ernest & Akpoti, Komlavi & Antwi Ofosu, Eric, 2022. "Modeling climate change impact on inflow and hydropower generation of Nangbeto dam in West Africa using multi-model CORDEX ensemble and ensemble machine learning," Applied Energy, Elsevier, vol. 325(C).
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