Hydropower Plant Available Energy Forecasting Using Artificial Neural Network and Particle Swarm Optimization
Accurate forecasting of the available energy portion that corresponds to the reservoir inflow of the month(s) ahead provides important decision support for hydropower plants in energy production planning for revenue maximization, as well as for environmental impact prevention and flood control upstr...
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MDPI AG
2024-10-01
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| Series: | Electricity |
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| Online Access: | https://www.mdpi.com/2673-4826/5/4/37 |
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| author | Suriya Kaewarsa Vanhkham Kongpaseuth |
| author_facet | Suriya Kaewarsa Vanhkham Kongpaseuth |
| author_sort | Suriya Kaewarsa |
| collection | DOAJ |
| description | Accurate forecasting of the available energy portion that corresponds to the reservoir inflow of the month(s) ahead provides important decision support for hydropower plants in energy production planning for revenue maximization, as well as for environmental impact prevention and flood control upstream and downstream of a basin. Therefore, a reliable forecasting tool or model is deemed necessary and crucial. Considering the fluctuation and nonlinearity of data which significantly influence the forecasting results, this study develops an effective hybrid model by integrating an Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) called “PSO-ANN” model based on the hydrological and meteorological data pre-processed by cross-correlation function (CCF), autocorrelation function (AFC), and normalization techniques for predicting the available energy portion corresponding to the reservoir inflow mentioned above for a case study hydropower plant in Laos, namely, the Theun-Hinboun hydropower plant (THHP). The model was evaluated by using correlation coefficient (<i>r</i>), relative error (<i>RE</i>), root mean square error (<i>RMSE</i>), and Taylor diagram plots in comparison with popular single-algorithm approaches such as ANN, and NARX models. The results demonstrated the superiority of the proposed PSO-ANN approach over the other two models, in addition to being comparable to those proposed by previous studies. |
| format | Article |
| id | doaj-art-c9c118f6878a4a9b9e284769b2f2c224 |
| institution | Kabale University |
| issn | 2673-4826 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electricity |
| spelling | doaj-art-c9c118f6878a4a9b9e284769b2f2c2242024-12-27T14:22:50ZengMDPI AGElectricity2673-48262024-10-015475176910.3390/electricity5040037Hydropower Plant Available Energy Forecasting Using Artificial Neural Network and Particle Swarm OptimizationSuriya Kaewarsa0Vanhkham Kongpaseuth1Department of Electrical Engineering, Faculty of Industry and Technology, Rajamangala University of Technology Isan, Sakon Nakhon Campus, Sakon Nakhon 47160, ThailandDepartment of Electrical Engineering, Faculty of Industry and Technology, Rajamangala University of Technology Isan, Sakon Nakhon Campus, Sakon Nakhon 47160, ThailandAccurate forecasting of the available energy portion that corresponds to the reservoir inflow of the month(s) ahead provides important decision support for hydropower plants in energy production planning for revenue maximization, as well as for environmental impact prevention and flood control upstream and downstream of a basin. Therefore, a reliable forecasting tool or model is deemed necessary and crucial. Considering the fluctuation and nonlinearity of data which significantly influence the forecasting results, this study develops an effective hybrid model by integrating an Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) called “PSO-ANN” model based on the hydrological and meteorological data pre-processed by cross-correlation function (CCF), autocorrelation function (AFC), and normalization techniques for predicting the available energy portion corresponding to the reservoir inflow mentioned above for a case study hydropower plant in Laos, namely, the Theun-Hinboun hydropower plant (THHP). The model was evaluated by using correlation coefficient (<i>r</i>), relative error (<i>RE</i>), root mean square error (<i>RMSE</i>), and Taylor diagram plots in comparison with popular single-algorithm approaches such as ANN, and NARX models. The results demonstrated the superiority of the proposed PSO-ANN approach over the other two models, in addition to being comparable to those proposed by previous studies.https://www.mdpi.com/2673-4826/5/4/37energy predictionreservoir inflow forecastingdeep learningartificial neural networkparticle swarm optimizationPSO-ANN |
| spellingShingle | Suriya Kaewarsa Vanhkham Kongpaseuth Hydropower Plant Available Energy Forecasting Using Artificial Neural Network and Particle Swarm Optimization Electricity energy prediction reservoir inflow forecasting deep learning artificial neural network particle swarm optimization PSO-ANN |
| title | Hydropower Plant Available Energy Forecasting Using Artificial Neural Network and Particle Swarm Optimization |
| title_full | Hydropower Plant Available Energy Forecasting Using Artificial Neural Network and Particle Swarm Optimization |
| title_fullStr | Hydropower Plant Available Energy Forecasting Using Artificial Neural Network and Particle Swarm Optimization |
| title_full_unstemmed | Hydropower Plant Available Energy Forecasting Using Artificial Neural Network and Particle Swarm Optimization |
| title_short | Hydropower Plant Available Energy Forecasting Using Artificial Neural Network and Particle Swarm Optimization |
| title_sort | hydropower plant available energy forecasting using artificial neural network and particle swarm optimization |
| topic | energy prediction reservoir inflow forecasting deep learning artificial neural network particle swarm optimization PSO-ANN |
| url | https://www.mdpi.com/2673-4826/5/4/37 |
| work_keys_str_mv | AT suriyakaewarsa hydropowerplantavailableenergyforecastingusingartificialneuralnetworkandparticleswarmoptimization AT vanhkhamkongpaseuth hydropowerplantavailableenergyforecastingusingartificialneuralnetworkandparticleswarmoptimization |