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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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Metadata
Document Title
Comparison of power output forecasting on the photovoltaic system using adaptive neuro-fuzzy inference systems and particle swarm optimization-artificial neural network model
Author
Dawan P., Sriprapha K., Kittisontirak S., Boonraksa T., Junhuathon N., Titiroongruang W., Niemcharoen S.
Name from Authors Collection
Affiliations
Department of Electrical Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, 10520, Thailand; Solar energy technology laboratory, National Electronics, Computer Technology Center, National Science and Technology Development Agency (NSTDA), Pathum Thani, 12120, Thailand; School of Electrical Engineering, Faculty of Engineering, Rajamangala University of Technology Rattanakosin, Nakhon Pathom, 73170, Thailand; School of Electrical Engineering, Faculty of Engineering, Bangkok Thonburi University, Bankok, 10170, Thailand
Type
Article
Source Title
Energies
ISSN
19961073
Year
2020
Volume
13
Issue
2
Open Access
Gold
Publisher
MDPI AG
DOI
10.3390/en13020351
Format
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. © 2020 by the authors.
Funding Sponsor
National Science and Technology Development Agency; King Mongkut's Institute of Technology Ladkrabang; National Electronics and Computer Technology Center
License
CC BY
Rights
Author
Publication Source
Scopus
Note
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