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Performance Analysis of Statistical, Machine Learning and Deep Learning Models in Long-Term Forecasting of Solar Power Production

Author

Listed:
  • Ashish Sedai

    (National Wind Institute, Texas Tech University, Lubbock, TX 79415, USA)

  • Rabin Dhakal

    (Electric Power Research Institute, Palo Alto, CA 94304, USA)

  • Shishir Gautam

    (Department of Mechanical Engineering, Tribhuvan University, Dharan 56700, Nepal)

  • Anibesh Dhamala

    (Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79401, USA)

  • Argenis Bilbao

    (National Wind Institute, Texas Tech University, Lubbock, TX 79415, USA)

  • Qin Wang

    (Electric Power Research Institute, Palo Alto, CA 94304, USA)

  • Adam Wigington

    (Electric Power Research Institute, Palo Alto, CA 94304, USA)

  • Suhas Pol

    (National Wind Institute, Texas Tech University, Lubbock, TX 79415, USA
    Renewable Energy Program, Texas Tech University, Lubbock, TX 79401, USA)

Abstract

The Machine Learning/Deep Learning (ML/DL) forecasting model has helped stakeholders overcome uncertainties associated with renewable energy resources and time planning for probable near-term power fluctuations. Nevertheless, the effectiveness of long-term forecasting of renewable energy resources using an existing ML/DL model is still debatable and needs additional research. Considering the constraints inherent in current empirical or physical-based forecasting models, the study utilizes ML/DL models to provide long-term predictions for solar power production. This study aims to examine the efficacy of several existing forecasting models. The study suggests approaches to enhance the accuracy of long-term forecasting of solar power generation for a case study power plant. It summarizes and compares the statistical model (ARIMA), ML model (SVR), DL models (LSTM, GRU, etc.), and ensemble models (RF, hybrid) with respect to long-term prediction. The performances of the univariate and multivariate models are summarized and compared based on their ability to accurately predict solar power generation for the next 1, 3, 5, and 15 days for a 100-kW solar power plant in Lubbock, TX, USA. Conclusions are drawn predicting the accuracy of various model changes with variation in the prediction time frame and input variables. In summary, the Random Forest model predicted long-term solar power generation with 50% better accuracy over the univariate statistical model and 10% better accuracy over multivariate ML/DL models.

Suggested Citation

  • Ashish Sedai & Rabin Dhakal & Shishir Gautam & Anibesh Dhamala & Argenis Bilbao & Qin Wang & Adam Wigington & Suhas Pol, 2023. "Performance Analysis of Statistical, Machine Learning and Deep Learning Models in Long-Term Forecasting of Solar Power Production," Forecasting, MDPI, vol. 5(1), pages 1-29, February.
    • Handle: RePEc:gam:jforec:v:5:y:2023:i:1:p:14-284:d:1076904
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    References listed on IDEAS

    as
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    Cited by:

    1. Shahad Mohammed Radhi & Sadeq D. Al-Majidi & Maysam F. Abbod & Hamed S. Al-Raweshidy, 2024. "Machine Learning Approaches for Short-Term Photovoltaic Power Forecasting," Energies, MDPI, vol. 17(17), pages 1-23, August.

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