Version 1
: Received: 21 July 2024 / Approved: 22 July 2024 / Online: 25 July 2024 (12:58:56 CEST)
How to cite:
Kaewarsa, S.; Kongpaseuth, V. Hydropower Plant Available Energy Forecasting Using Artificial Neural Network and Particle Swarm Optimization. Preprints2024, 2024072024. https://doi.org/10.20944/preprints202407.2024.v1
Kaewarsa, S.; Kongpaseuth, V. Hydropower Plant Available Energy Forecasting Using Artificial Neural Network and Particle Swarm Optimization. Preprints 2024, 2024072024. https://doi.org/10.20944/preprints202407.2024.v1
Kaewarsa, S.; Kongpaseuth, V. Hydropower Plant Available Energy Forecasting Using Artificial Neural Network and Particle Swarm Optimization. Preprints2024, 2024072024. https://doi.org/10.20944/preprints202407.2024.v1
APA Style
Kaewarsa, S., & Kongpaseuth, V. (2024). Hydropower Plant Available Energy Forecasting Using Artificial Neural Network and Particle Swarm Optimization. Preprints. https://doi.org/10.20944/preprints202407.2024.v1
Chicago/Turabian Style
Kaewarsa, S. and Vanhkham Kongpaseuth. 2024 "Hydropower Plant Available Energy Forecasting Using Artificial Neural Network and Particle Swarm Optimization" Preprints. https://doi.org/10.20944/preprints202407.2024.v1
Abstract
Accurate forecasting of available energy portion that corresponds to the reservoir inflow of the month(s) ahead provides important decision support for the hydropower plants in energy pro-duction planning for revenue maximization, as well as for the environmental impact prevention, and flood control at the 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 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, Theun-Hinboun hydropower plant (THHP). The model was evaluated by using correlation coefficient (r), relative error (RE), root mean square error (RMSE), and Taylor diagram plots in comparison with the popular single algorithm approaches such as ANN, and NARX models. Results demonstrated the superiority of the proposed PSO-ANN approach over the other two models.
Keywords
energy prediction; reservoir inflow forecasting; deep learning; artificial neural network; particle swarm optimization; PSO-ANN
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
