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Daily Power Generation Forecasting Method for a Group of Small Hydropower Stations Considering the Spatial and Temporal Distribution of Precipitation—South China Case Study

Author

Listed:
  • Shaojun Yang

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Hua Wei

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Le Zhang

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Shengchao Qin

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

Abstract

This paper proposes a multimodal deep learning method for forecasting the daily power generation of small hydropower stations that considers the temporal and spatial distribution of precipitation, which compensates for the shortcomings of traditional forecasting methods that do not consider differences in the spatial distribution of precipitation. First, the actual precipitation values measured by ground weather stations and the spatial distribution of precipitation observed by meteorological satellite remote sensing are used to complete the missing precipitation data through linear interpolation, and the gridded precipitation data covering a group of small hydropower stations are constructed. Then, considering the time lag between changes in the daily power generation of the group of small hydropower stations and precipitation, the partial mutual information method is used to estimate the “time difference” between the two, and combined with the precipitation grid data, a data set of the temporal and spatial distribution of precipitation is generated. Finally, using only the temporal and spatial distribution of precipitation and historical power generation data, a multimodal deep learning network based on a convolutional neural network (CNN) and multilayer perceptron (MLP) is constructed, and a highly accurate prediction model for the daily power generation of small hydropower stations is obtained. Taking the real power generation data of a group of small hydropower stations in southern China as an example, after considering the temporal and spatial distribution of precipitation, the prediction accuracy of the proposed method is as high as 93%, which is approximately 5.8% higher than before considering the temporal and spatial distribution of precipitation. In addition, compared with mainstream methods such as support vector regression (SVR) and the long–short-term memory network (LSTM) (the average accuracy is about 87%), and the average accuracy improvement of the proposed method is approximately 6%.

Suggested Citation

  • Shaojun Yang & Hua Wei & Le Zhang & Shengchao Qin, 2021. "Daily Power Generation Forecasting Method for a Group of Small Hydropower Stations Considering the Spatial and Temporal Distribution of Precipitation—South China Case Study," Energies, MDPI, vol. 14(15), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4387-:d:598094
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    References listed on IDEAS

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    1. Gang Li & Bao-Jian Li & Xu-Guang Yu & Chun-Tian Cheng, 2015. "Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants," Energies, MDPI, vol. 8(10), pages 1-14, October.
    2. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    3. Kong, Yigang & Wang, Jie & Kong, Zhigang & Song, Furong & Liu, Zhiqi & Wei, Congmei, 2015. "Small hydropower in China: The survey and sustainable future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 425-433.
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    Cited by:

    1. Geoffrey Gasore & Arthur Santos & Etienne Ntagwirumugara & Daniel Zimmerle, 2023. "Sizing of Small Hydropower Plants for Highly Variable Flows in Tropical Run-of-River Installations: A Case Study of the Sebeya River," Energies, MDPI, vol. 16(3), pages 1-14, January.
    2. Eric Stefan Miele & Nicole Ludwig & Alessandro Corsini, 2023. "Multi-Horizon Wind Power Forecasting Using Multi-Modal Spatio-Temporal Neural Networks," Energies, MDPI, vol. 16(8), pages 1-15, April.

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