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Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

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  • Chao Huang

    (School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China
    Key Laboratory of Wind Energy and Solar Energy Technology (Inner Mongolia University of Technology), Ministry of Education, Hohhot 010051, China
    Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China)

  • Longpeng Cao

    (School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China
    Key Laboratory of Wind Energy and Solar Energy Technology (Inner Mongolia University of Technology), Ministry of Education, Hohhot 010051, China
    Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China)

  • Nanxin Peng

    (School of International Business, Southwestern University of Finance and Economics, Chengdu 611130, China)

  • Sijia Li

    (National Internet Finance Association of China, Beijing 100080, China)

  • Jing Zhang

    (School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China
    Key Laboratory of Wind Energy and Solar Energy Technology (Inner Mongolia University of Technology), Ministry of Education, Hohhot 010051, China
    Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China)

  • Long Wang

    (School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China
    Key Laboratory of Wind Energy and Solar Energy Technology (Inner Mongolia University of Technology), Ministry of Education, Hohhot 010051, China
    Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China)

  • Xiong Luo

    (School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China
    Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China)

  • Jenq-Haur Wang

    (Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei City 106, Taiwan)

Abstract

Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

Suggested Citation

  • Chao Huang & Longpeng Cao & Nanxin Peng & Sijia Li & Jing Zhang & Long Wang & Xiong Luo & Jenq-Haur Wang, 2018. "Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception," Sustainability, MDPI, vol. 10(12), pages 1-8, December.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:12:p:4863-:d:191858
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    References listed on IDEAS

    as
    1. Huang, Chao & Bensoussan, Alain & Edesess, Michael & Tsui, Kwok L., 2016. "Improvement in artificial neural network-based estimation of grid connected photovoltaic power output," Renewable Energy, Elsevier, vol. 97(C), pages 838-848.
    2. Leva, S. & Dolara, A. & Grimaccia, F. & Mussetta, M. & Ogliari, E., 2017. "Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 88-100.
    3. Li, Yanting & Su, Yan & Shu, Lianjie, 2014. "An ARMAX model for forecasting the power output of a grid connected photovoltaic system," Renewable Energy, Elsevier, vol. 66(C), pages 78-89.
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    Cited by:

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    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. Thi Ngoc Nguyen & Felix Musgens, 2021. "What drives the accuracy of PV output forecasts?," Papers 2111.02092, arXiv.org.
    4. Nguyen, Thi Ngoc & Müsgens, Felix, 2022. "What drives the accuracy of PV output forecasts?," Applied Energy, Elsevier, vol. 323(C).
    5. Niu, Yunbo & Wang, Jianzhou & Zhang, Ziyuan & Luo, Tianrui & Liu, Jingjiang, 2024. "De-Trend First, Attend Next: A Mid-Term PV forecasting system with attention mechanism and encoder–decoder structure," Applied Energy, Elsevier, vol. 353(PB).

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