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Hour-Ahead Photovoltaic Power Forecasting Using an Analog Plus Neural Network Ensemble Method

Author

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  • Jingyue Wang

    (School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China)

  • Zheng Qian

    (School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China)

  • Jingyi Wang

    (School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
    This Author’s Current Affiliation is Fujitsu Research & Development Center Co., Ltd., Beijing 100025, China.)

  • Yan Pei

    (State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China)

Abstract

The common analog approach and ensemble methods in photovoltaic (PV) power forecasting are based on the forecasts from several numerical weather prediction (NWP) models. These may be not applicable to the very-short-term PV power forecasting, since forecasts based on NWP models are reliable in horizons longer than six hours. In this paper, a methodology for one-hour-ahead PV power forecasting is proposed. Instead of the NWP models, the persistence method is applied in the analog approach to produce meteorological forecasts. The historical data with meteorological predictions similar to the target forecast hour are identified to train the forecast model. Then, the feed forward neural networks (FNNs) act as the base predictors of the neural network ensemble method to replace the NWP-based PV power prediction methods. The forecast results produced by the FNNs are combined by the random forest (RF) algorithm. The performance of the proposed method is evaluated on a real grid-connected PV plant located in Southeast China. Results show that the proposed method outperforms six benchmark models: the persistence model, the support vector regression (SVR) model, the linear regression model, the RF model, the gradient boosting model, and XGBoost model. The improvements reach up to over 40% for the standard error metrics.

Suggested Citation

  • Jingyue Wang & Zheng Qian & Jingyi Wang & Yan Pei, 2020. "Hour-Ahead Photovoltaic Power Forecasting Using an Analog Plus Neural Network Ensemble Method," Energies, MDPI, vol. 13(12), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3259-:d:375564
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    References listed on IDEAS

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    1. Wang, Jing-Yi & Qian, Zheng & Zareipour, Hamidreza & Wood, David, 2018. "Performance assessment of photovoltaic modules based on daily energy generation estimation," Energy, Elsevier, vol. 165(PB), pages 1160-1172.
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    4. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
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    Cited by:

    1. Aleksander Radovan & Viktor Šunde & Danijel Kučak & Željko Ban, 2021. "Solar Irradiance Forecast Based on Cloud Movement Prediction," Energies, MDPI, vol. 14(13), pages 1-25, June.
    2. 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).
    3. Nguyen, Thi Ngoc & Müsgens, Felix, 2022. "What drives the accuracy of PV output forecasts?," Applied Energy, Elsevier, vol. 323(C).
    4. Andi A. H. Lateko & Hong-Tzer Yang & Chao-Ming Huang, 2022. "Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method," Energies, MDPI, vol. 15(11), pages 1-21, June.
    5. Thi Ngoc Nguyen & Felix Musgens, 2021. "What drives the accuracy of PV output forecasts?," Papers 2111.02092, arXiv.org.
    6. Sarmas, Elissaios & Spiliotis, Evangelos & Stamatopoulos, Efstathios & Marinakis, Vangelis & Doukas, Haris, 2023. "Short-term photovoltaic power forecasting using meta-learning and numerical weather prediction independent Long Short-Term Memory models," Renewable Energy, Elsevier, vol. 216(C).

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