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A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network

Author

Listed:
  • Emanuele Ogliari

    (Dipartimento di Energia, Politecnico di Milano, via La Masa 34, 20156 Milano, Italy
    These authors contributed equally to this work.)

  • Alfredo Nespoli

    (Dipartimento di Energia, Politecnico di Milano, via La Masa 34, 20156 Milano, Italy
    These authors contributed equally to this work.)

  • Marco Mussetta

    (Dipartimento di Energia, Politecnico di Milano, via La Masa 34, 20156 Milano, Italy
    These authors contributed equally to this work.)

  • Silvia Pretto

    (Dipartimento di Energia, Politecnico di Milano, via La Masa 34, 20156 Milano, Italy
    These authors contributed equally to this work.)

  • Andrea Zimbardo

    (Dipartimento di Energia, Politecnico di Milano, via La Masa 34, 20156 Milano, Italy
    These authors contributed equally to this work.)

  • Nicholas Bonfanti

    (Milano Multiphysics S.r.l.s, Polihub, via Durando 39, 20158 Milano, Italy)

  • Manuele Aufiero

    (Milano Multiphysics S.r.l.s, Polihub, via Durando 39, 20158 Milano, Italy)

Abstract

The increasing penetration of non-programmable renewable energy sources (RES) is enforcing the need for accurate power production forecasts. In the category of hydroelectric plants, Run of the River (RoR) plants belong to the class of non-programmable RES. Data-driven models are nowadays the most widely adopted methodologies in hydropower forecast. Among all, the Artificial Neural Network (ANN) proved to be highly successful in production forecast. Widely adopted and equally important for hydropower generation forecast is the HYdrological Predictions for the Environment (HYPE), a semi-distributed hydrological Rainfall–Runoff model. A novel hybrid method, providing HYPE sub-basins flow computation as input to an ANN, is here introduced and tested both with and without the adoption of a decomposition approach. In the former case, two ANNs are trained to forecast the trend and the residual of the production, respectively, to be then summed up to the previously extracted seasonality component and get the power forecast. These results have been compared to those obtained from the adoption of a ANN with rainfalls in input, again with and without decomposition approach. The methods have been assessed by forecasting the Run-of-the-River hydroelectric power plant energy for the year 2017. Besides, the forecasts of 15 power plants output have been fairly compared in order to identify the most accurate forecasting technique. The here proposed hybrid method (HYPE and ANN) has shown to be the most accurate in all the considered study cases.

Suggested Citation

  • Emanuele Ogliari & Alfredo Nespoli & Marco Mussetta & Silvia Pretto & Andrea Zimbardo & Nicholas Bonfanti & Manuele Aufiero, 2020. "A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network," Forecasting, MDPI, vol. 2(4), pages 1-19, October.
    • Handle: RePEc:gam:jforec:v:2:y:2020:i:4:p:22-428:d:428737
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    References listed on IDEAS

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    1. Simone Sala & Alfonso Amendola & Sonia Leva & Marco Mussetta & Alessandro Niccolai & Emanuele Ogliari, 2019. "Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant," Energies, MDPI, vol. 12(23), pages 1-19, November.
    2. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    3. Monteiro, Claudio & Ramirez-Rosado, Ignacio J. & Fernandez-Jimenez, L. Alfredo, 2013. "Short-term forecasting model for electric power production of small-hydro power plants," Renewable Energy, Elsevier, vol. 50(C), pages 387-394.
    4. Wang, Shuai & Yu, Lean & Tang, Ling & Wang, Shouyang, 2011. "A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China," Energy, Elsevier, vol. 36(11), pages 6542-6554.
    5. Miller, Don M. & Williams, Dan, 2003. "Shrinkage estimators of time series seasonal factors and their effect on forecasting accuracy," International Journal of Forecasting, Elsevier, vol. 19(4), pages 669-684.
    6. Zhang, G. Peter & Qi, Min, 2005. "Neural network forecasting for seasonal and trend time series," European Journal of Operational Research, Elsevier, vol. 160(2), pages 501-514, January.
    7. Murat Cobaner & Tefaruk Haktanir & Ozgur Kisi, 2008. "Prediction of Hydropower Energy Using ANN for the Feasibility of Hydropower Plant Installation to an Existing Irrigation Dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(6), pages 757-774, June.
    8. Gaudard, Ludovic & Avanzi, Francesco & De Michele, Carlo, 2018. "Seasonal aspects of the energy-water nexus: The case of a run-of-the-river hydropower plant," Applied Energy, Elsevier, vol. 210(C), pages 604-612.
    9. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
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    Cited by:

    1. Marcos Tadeu Barros de Oliveira & Patrícia de Sousa Oliveira Silva & Elisa Oliveira & André Luís Marques Marcato & Giovani Santiago Junqueira, 2021. "Availability Projections of Hydroelectric Power Plants through Monte Carlo Simulation," Energies, MDPI, vol. 14(24), pages 1-18, December.
    2. Marlene A. Perez-Villalpando & Kelly J. Gurubel Tun & Carlos A. Arellano-Muro & Fernando Fausto, 2021. "Inverse Optimal Control Using Metaheuristics of Hydropower Plant Model via Forecasting Based on the Feature Engineering," Energies, MDPI, vol. 14(21), pages 1-18, November.

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