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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
    1. Jihoon Moon & Yongsung Kim & Minjae Son & Eenjun Hwang, 2018. "Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron," Energies, MDPI, vol. 11(12), pages 1-20, November.
    2. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Zeng, Wenzhi & Wang, Xiukang & Zou, Haiyang, 2019. "Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 186-212.
    3. Hyeong Kyu Choi, 2018. "Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model," Papers 1808.01560, arXiv.org, revised Oct 2018.
    4. 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).
    5. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    6. Hyung Keun Ahn & Neungsoo Park, 2021. "Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors," Energies, MDPI, vol. 14(2), pages 1-17, January.
    7. Li, Pengtao & Zhou, Kaile & Lu, Xinhui & Yang, Shanlin, 2020. "A hybrid deep learning model for short-term PV power forecasting," Applied Energy, Elsevier, vol. 259(C).
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