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Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

Author

Listed:
  • Hossam Fraihat

    (Department of Electrical Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan)

  • Amneh A. Almbaideen

    (Department of Electrical Engineering, Mutah University, Al Karak 61710, Jordan)

  • Abdullah Al-Odienat

    (Department of Electrical Engineering, Mutah University, Al Karak 61710, Jordan)

  • Bassam Al-Naami

    (Department of Biomedical Engineering, Engineering Faculty, The Hashemite University, Zarqa 13133, Jordan)

  • Roberto De Fazio

    (Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy)

  • Paolo Visconti

    (Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy)

Abstract

Solar energy is one of the most important renewable energies, with many advantages over other sources. Many parameters affect the electricity generation from solar plants. This paper aims to study the influence of these parameters on predicting solar radiation and electric energy produced in the Salt-Jordan region (Middle East) using long short-term memory (LSTM) and Adaptive Network-based Fuzzy Inference System (ANFIS) models. The data relating to 24 meteorological parameters for nearly the past five years were downloaded from the MeteoBleu database. The results show that the influence of parameters on solar radiation varies according to the season. The forecasting using ANFIS provides better results when the parameter correlation with solar radiation is high (i.e., Pearson Correlation Coefficient PCC between 0.95 and 1). In comparison, the LSTM neural network shows better results when correlation is low (PCC in the range 0.5–0.8). The obtained RMSE varies from 0.04 to 0.8 depending on the season and used parameters; new meteorological parameters influencing solar radiation are also investigated.

Suggested Citation

  • Hossam Fraihat & Amneh A. Almbaideen & Abdullah Al-Odienat & Bassam Al-Naami & Roberto De Fazio & Paolo Visconti, 2022. "Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan," Future Internet, MDPI, vol. 14(3), pages 1-24, March.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:3:p:79-:d:764972
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    Cited by:

    1. Chen, Yunxiao & Bai, Mingliang & Zhang, Yilan & Liu, Jinfu & Yu, Daren, 2023. "Proactively selection of input variables based on information gain factors for deep learning models in short-term solar irradiance forecasting," Energy, Elsevier, vol. 284(C).
    2. Maksymilian Mądziel & Tiziana Campisi, 2023. "Energy Consumption of Electric Vehicles: Analysis of Selected Parameters Based on Created Database," Energies, MDPI, vol. 16(3), pages 1-18, February.
    3. Azizi, Narjes & Yaghoubirad, Maryam & Farajollahi, Meisam & Ahmadi, Abolfzl, 2023. "Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output," Renewable Energy, Elsevier, vol. 206(C), pages 135-147.

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