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

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Department of Electrical Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
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Solar energy technology laboratory, National Electronics, and Computer Technology Center, National Science and Technology Development Agency (NSTDA), Pathum Thani 12120, Thailand
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School of Electrical Engineering, Faculty of Engineering, Rajamangala University of Technology Rattanakosin, Nakhon Pathom 73170, Thailand
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School of Electrical Engineering, Faculty of Engineering, Bangkok Thonburi University, Bankok 10170, Thailand
*
Author to whom correspondence should be addressed.
Energies 2020, 13(2), 351; https://doi.org/10.3390/en13020351
Received: 15 November 2019 / Revised: 24 December 2019 / Accepted: 27 December 2019 / Published: 10 January 2020
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.
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Keywords: PVs power output forecasting; adaptive neuro-fuzzy inference systems; particle swarm optimization-artificial neural networks; solar irradiation PVs power output forecasting; adaptive neuro-fuzzy inference systems; particle swarm optimization-artificial neural networks; solar irradiation
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Dawan, P.; Sriprapha, K.; Kittisontirak, S.; Boonraksa, T.; Junhuathon, N.; Titiroongruang, W.; Niemcharoen, S. Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model. Energies 2020, 13, 351.

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