Hour-Ahead Photovoltaic Output Forecasting Using Wavelet-ANFIS

Chao-Rong Chen, Faouzi Brice Ouedraogo, Yu-Ming Chang, Devita Ayu Larasati and Shih-Wei Tan
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Chao-Rong Chen: Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Faouzi Brice Ouedraogo: International Program of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei 10608, Taiwan
Yu-Ming Chang: Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Devita Ayu Larasati: Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Shih-Wei Tan: Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan

Mathematics, 2021, vol. 9, issue 19, 1-14

Abstract: The operational challenge of a photovoltaic (PV) integrated system is the uncertainty (irregularity) of the future power output. The integration and correct operation can be carried out with accurate forecasting of the PV output power. A distinct artificial intelligence method was employed in the present study to forecast the PV output power and investigate the accuracy using endogenous data. Discrete wavelet transforms were used to decompose PV output power into approximate and detailed components. The decomposed PV output was fed into an adaptive neuro-fuzzy inference system (ANFIS) input model to forecast the short-term PV power output. Various wavelet mother functions were also investigated, including Haar, Daubechies, Coiflets, and Symlets. The proposed model performance was highly correlated to the input set and wavelet mother function. The statistical performance of the wavelet-ANFIS was found to have better efficiency compared with the ANFIS and ANN models. In addition, wavelet-ANFIS coif2 and sym4 offer the best precision among all the studied models. The result highlights that the combination of wavelet decomposition and the ANFIS model can be a helpful tool for accurate short-term PV output forecasting and yield better efficiency and performance than the conventional model.

Keywords: PV forecasting; ANFIS; wavelet-ANFIS; wavelet decomposition; mother wavelet function (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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