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Hour-Ahead Photovoltaic Output Forecasting Using Wavelet-ANFIS

Author

Listed:
  • 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)

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.

Suggested Citation

  • Chao-Rong Chen & Faouzi Brice Ouedraogo & Yu-Ming Chang & Devita Ayu Larasati & Shih-Wei Tan, 2021. "Hour-Ahead Photovoltaic Output Forecasting Using Wavelet-ANFIS," Mathematics, MDPI, vol. 9(19), pages 1-14, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:19:p:2438-:d:648136
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    References listed on IDEAS

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    1. Sabadus, Andreea & Blaga, Robert & Hategan, Sergiu-Mihai & Calinoiu, Delia & Paulescu, Eugenia & Mares, Oana & Boata, Remus & Stefu, Nicoleta & Paulescu, Marius & Badescu, Viorel, 2024. "A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches," Renewable Energy, Elsevier, vol. 226(C).

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