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

Emanuele Ogliari, Alfredo Nespoli, Marco Mussetta, Silvia Pretto, Andrea Zimbardo, Nicholas Bonfanti and Manuele Aufiero
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Emanuele Ogliari: Dipartimento di Energia, Politecnico di Milano, via La Masa 34, 20156 Milano, Italy
Alfredo Nespoli: Dipartimento di Energia, Politecnico di Milano, via La Masa 34, 20156 Milano, Italy
Marco Mussetta: Dipartimento di Energia, Politecnico di Milano, via La Masa 34, 20156 Milano, Italy
Silvia Pretto: Dipartimento di Energia, Politecnico di Milano, via La Masa 34, 20156 Milano, Italy
Andrea Zimbardo: Dipartimento di Energia, Politecnico di Milano, via La Masa 34, 20156 Milano, Italy
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

Forecasting, 2020, vol. 2, issue 4, 1-19

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.

Keywords: hydropower production forecast; Artificial Neural Networks; run of the river hydroelectric plants; seasonal decomposition; HYPE model; RES generation (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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