A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network
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 wi...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-10-01
|
| Series: | Forecasting |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-9394/2/4/22 |
| _version_ | 1827704246140665856 |
|---|---|
| author | Emanuele Ogliari Alfredo Nespoli Marco Mussetta Silvia Pretto Andrea Zimbardo Nicholas Bonfanti Manuele Aufiero |
| author_facet | Emanuele Ogliari Alfredo Nespoli Marco Mussetta Silvia Pretto Andrea Zimbardo Nicholas Bonfanti Manuele Aufiero |
| author_sort | Emanuele Ogliari |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T15:35:59Z |
| format | Article |
| id | doaj.art-eec7e303be094ea7ad04d5757f450ec6 |
| institution | Directory Open Access Journal |
| issn | 2571-9394 |
| language | English |
| last_indexed | 2024-03-10T15:35:59Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forecasting |
| spelling | doaj.art-eec7e303be094ea7ad04d5757f450ec62023-11-20T17:16:22ZengMDPI AGForecasting2571-93942020-10-012441042810.3390/forecast2040022A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural NetworkEmanuele Ogliari0Alfredo Nespoli1Marco Mussetta2Silvia Pretto3Andrea Zimbardo4Nicholas Bonfanti5Manuele Aufiero6Dipartimento di Energia, Politecnico di Milano, via La Masa 34, 20156 Milano, ItalyDipartimento di Energia, Politecnico di Milano, via La Masa 34, 20156 Milano, ItalyDipartimento di Energia, Politecnico di Milano, via La Masa 34, 20156 Milano, ItalyDipartimento di Energia, Politecnico di Milano, via La Masa 34, 20156 Milano, ItalyDipartimento di Energia, Politecnico di Milano, via La Masa 34, 20156 Milano, ItalyMilano Multiphysics S.r.l.s, Polihub, via Durando 39, 20158 Milano, ItalyMilano Multiphysics S.r.l.s, Polihub, via Durando 39, 20158 Milano, ItalyThe 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.https://www.mdpi.com/2571-9394/2/4/22hydropower production forecastArtificial Neural Networksrun of the river hydroelectric plantsseasonal decompositionHYPE modelRES generation |
| spellingShingle | Emanuele Ogliari Alfredo Nespoli Marco Mussetta Silvia Pretto Andrea Zimbardo Nicholas Bonfanti Manuele Aufiero A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network Forecasting hydropower production forecast Artificial Neural Networks run of the river hydroelectric plants seasonal decomposition HYPE model RES generation |
| title | A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network |
| title_full | A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network |
| title_fullStr | A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network |
| title_full_unstemmed | A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network |
| title_short | A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network |
| title_sort | hybrid method for the run of the river hydroelectric power plant energy forecast hype hydrological model and neural network |
| topic | hydropower production forecast Artificial Neural Networks run of the river hydroelectric plants seasonal decomposition HYPE model RES generation |
| url | https://www.mdpi.com/2571-9394/2/4/22 |
| work_keys_str_mv | AT emanueleogliari ahybridmethodfortherunoftheriverhydroelectricpowerplantenergyforecasthypehydrologicalmodelandneuralnetwork AT alfredonespoli ahybridmethodfortherunoftheriverhydroelectricpowerplantenergyforecasthypehydrologicalmodelandneuralnetwork AT marcomussetta ahybridmethodfortherunoftheriverhydroelectricpowerplantenergyforecasthypehydrologicalmodelandneuralnetwork AT silviapretto ahybridmethodfortherunoftheriverhydroelectricpowerplantenergyforecasthypehydrologicalmodelandneuralnetwork AT andreazimbardo ahybridmethodfortherunoftheriverhydroelectricpowerplantenergyforecasthypehydrologicalmodelandneuralnetwork AT nicholasbonfanti ahybridmethodfortherunoftheriverhydroelectricpowerplantenergyforecasthypehydrologicalmodelandneuralnetwork AT manueleaufiero ahybridmethodfortherunoftheriverhydroelectricpowerplantenergyforecasthypehydrologicalmodelandneuralnetwork AT emanueleogliari hybridmethodfortherunoftheriverhydroelectricpowerplantenergyforecasthypehydrologicalmodelandneuralnetwork AT alfredonespoli hybridmethodfortherunoftheriverhydroelectricpowerplantenergyforecasthypehydrologicalmodelandneuralnetwork AT marcomussetta hybridmethodfortherunoftheriverhydroelectricpowerplantenergyforecasthypehydrologicalmodelandneuralnetwork AT silviapretto hybridmethodfortherunoftheriverhydroelectricpowerplantenergyforecasthypehydrologicalmodelandneuralnetwork AT andreazimbardo hybridmethodfortherunoftheriverhydroelectricpowerplantenergyforecasthypehydrologicalmodelandneuralnetwork AT nicholasbonfanti hybridmethodfortherunoftheriverhydroelectricpowerplantenergyforecasthypehydrologicalmodelandneuralnetwork AT manueleaufiero hybridmethodfortherunoftheriverhydroelectricpowerplantenergyforecasthypehydrologicalmodelandneuralnetwork |