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Short-Term Prediction of PV Power Based on Combined Modal Decomposition and NARX-LSTM-LightGBM

Author

Listed:
  • Hongbo Gao

    (School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Shuang Qiu

    (School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Jun Fang

    (School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Nan Ma

    (School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Jiye Wang

    (School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Kun Cheng

    (School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Hui Wang

    (School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Yidong Zhu

    (Electric Power Research Institute, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110001, China)

  • Dawei Hu

    (Electric Power Research Institute, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110001, China)

  • Hengyu Liu

    (Electric Power Research Institute, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110001, China)

  • Jun Wang

    (School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

Abstract

Recently, solar energy has been gaining attention as one of the best promising renewable energy sources. Accurate PV power prediction models can solve the impact on the power system due to the non-linearity and randomness of PV power generation and play a crucial role in the operation and scheduling of power plants. This paper proposes a novel machine learning network framework to predict short-term PV power in a time-series manner. The combination of nonlinear auto-regressive neural networks with exogenous input (NARX), long short term memory (LSTM) neural network, and light gradient boosting machine (LightGBM) prediction model (NARX-LSTM-LightGBM) was constructed based on the combined modal decomposition. Specifically, this paper uses a dataset that includes ambient temperature, irradiance, inverter temperature, module temperature, etc. Firstly, the feature variables with high correlation effects on PV power were selected by Pearson correlation analysis. Furthermore, the PV power is decomposed into a new feature matrix by (EMD), (EEMD) and (CEEMDAN), i.e., the combination decomposition (CD), which deeply explores the intrinsic connection of PV power historical series information and reduces the non-smoothness of PV power. Finally, preliminary PV power prediction values and error correction vector are obtained by NARX prediction. Both are embedded into the NARX-LSTM-LightGBM model pair for PV power prediction, and then the error inverse method is used for weighted optimization to improve the accuracy of the PV power prediction. The experiments were conducted with the measured data from Andre Agassi College, USA, and the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the model under different weather conditions were lower than 1.665 kw, 0.892 kw and 0.211, respectively, which are better than the prediction results of other models and proved the effectiveness of the model.

Suggested Citation

  • Hongbo Gao & Shuang Qiu & Jun Fang & Nan Ma & Jiye Wang & Kun Cheng & Hui Wang & Yidong Zhu & Dawei Hu & Hengyu Liu & Jun Wang, 2023. "Short-Term Prediction of PV Power Based on Combined Modal Decomposition and NARX-LSTM-LightGBM," Sustainability, MDPI, vol. 15(10), pages 1-22, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8266-:d:1150574
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    References listed on IDEAS

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    1. Gangqiang Li & Huaizhi Wang & Shengli Zhang & Jiantao Xin & Huichuan Liu, 2019. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach," Energies, MDPI, vol. 12(13), pages 1-17, July.
    2. Wenjie Zhang & Yuqiang Zhao & Fengcheng Huang & Yongheng Zhong & Jianwei Zhou, 2021. "Forecasting the Energy and Economic Benefits of Photovoltaic Technology in China’s Rural Areas," Sustainability, MDPI, vol. 13(15), pages 1-22, July.
    3. Gao, Mingming & Li, Jianjing & Hong, Feng & Long, Dongteng, 2019. "Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM," Energy, Elsevier, vol. 187(C).
    4. Chao Fu & Guo-Quan Li & Kuo-Ping Lin & Hui-Juan Zhang, 2019. "Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine," Sustainability, MDPI, vol. 11(2), pages 1-15, January.
    5. Mahesh Kumar & Amir Mahmood Soomro & Waqar Uddin & Laveet Kumar, 2022. "Optimal Multi-Objective Placement and Sizing of Distributed Generation in Distribution System: A Comprehensive Review," Energies, MDPI, vol. 15(21), pages 1-48, October.
    6. Hui Wang & Jianbo Sun & Weijun Wang, 2018. "Photovoltaic Power Forecasting Based on EEMD and a Variable-Weight Combination Forecasting Model," Sustainability, MDPI, vol. 10(8), pages 1-11, July.
    7. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, Elsevier, vol. 189(C).
    8. Singh, G.K., 2013. "Solar power generation by PV (photovoltaic) technology: A review," Energy, Elsevier, vol. 53(C), pages 1-13.
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    Cited by:

    1. Mohammad Abdul Baseer & Anas Almunif & Ibrahim Alsaduni & Nazia Tazeen, 2023. "Electrical Power Generation Forecasting from Renewable Energy Systems Using Artificial Intelligence Techniques," Energies, MDPI, vol. 16(18), pages 1-21, September.

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