Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model

Promphak Dawan, Kobsak Sriprapha, Songkiate Kittisontirak, Terapong Boonraksa, Nitikorn Junhuathon, Wisut Titiroongruang and Surasak Niemcharoen
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Promphak Dawan: Department of Electrical Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
Kobsak Sriprapha: Solar energy technology laboratory, National Electronics, and Computer Technology Center, National Science and Technology Development Agency (NSTDA), Pathum Thani 12120, Thailand
Songkiate Kittisontirak: Solar energy technology laboratory, National Electronics, and Computer Technology Center, National Science and Technology Development Agency (NSTDA), Pathum Thani 12120, Thailand
Terapong Boonraksa: School of Electrical Engineering, Faculty of Engineering, Rajamangala University of Technology Rattanakosin, Nakhon Pathom 73170, Thailand
Nitikorn Junhuathon: School of Electrical Engineering, Faculty of Engineering, Bangkok Thonburi University, Bankok 10170, Thailand
Wisut Titiroongruang: Department of Electrical Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
Surasak Niemcharoen: Department of Electrical Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand

Energies, 2020, vol. 13, issue 2, 1-18

Abstract: The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using adaptive neuro-fuzzy inference systems (ANFIS), comparing accuracy with particle swarm optimization combined with artificial neural network methods (PSO-ANN). The simulation results show that the forecasting with the ANFIS method is more accurate than the PSO-ANN method. The performance of the ANFIS and PSO-ANN models were verified with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAP) and mean absolute percent error (MAPE). The accuracy of the ANFIS model is 99.8532%, and the PSO-ANN method is 98.9157%. The power output forecast results of the model were evaluated and show that the proposed ANFIS forecasting method is more beneficial compared to the existing method for the computation of power output and investment decision making. Therefore, the analysis of the production of power output from PV systems is essential to be used for the most benefit and analysis of the investment cost.

Keywords: PVs power output forecasting; adaptive neuro-fuzzy inference systems; particle swarm optimization-artificial neural networks; solar irradiation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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