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Forecasting of Power Output of a PVPS Based on Meteorological Data Using RNN Approaches

Author

Listed:
  • Mohsen Beigi

    (Department of Mechanical Engineering, Tiran Branch, Islamic Azad University, Tiran 8531911111, Iran)

  • Hossein Beigi Harchegani

    (Institute for Higher Education, Academic Center for Education, Culture, and Research (ACECR), Ahvaz 6139688839, Iran)

  • Mehdi Torki

    (Department of Computer Engineering, Faculty of Electrical and Computer Engineering, Technical and Vocational University (TVU), Tehran 1435761137, Iran)

  • Mohammad Kaveh

    (Department of Petroleum Engineering, College of Engineering, Knowledge University, Erbil 44001, Iraq)

  • Mariusz Szymanek

    (Department of Agricultural, Forest and Transport Machinery, University of Life Sciences in Lublin, 20-612 Lubin, Poland)

  • Esmail Khalife

    (Department of Civil Engineering, Cihan University-Erbil, Kurdistan Region, Erbil 44001, Iraq)

  • Jacek Dziwulski

    (Department of Strategy and Business Planning, Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland)

Abstract

Artificial intelligence (AI) has become increasingly popular as a tool to model, identify, optimize, forecast, and control renewable energy systems. This work aimed to evaluate the capability of the artificial neural network (ANN) procedure to model and forecast solar power outputs of photovoltaic power systems (PVPSs) by using meteorological data. For this purpose, based on the literature review, important factors affecting energy generation in a PVPS were selected as inputs, and a recurrent neural network (RNN) architecture was established. After completing the trained network, the RNN capability was assessed to predict the energy output of the PVPS for days not included in the training database. The performance evaluation of the trained RNN revealed a regression value of 0.97774 for test data, whereas the RMSE and the mean actual output power for a sample day were 0.0248 MJ and 0.538 MJ, respectively. In addition to RMSE, an error histogram and regression plots obtained by MATLAB were employed to evaluate the network’s capability, and validation results represented a sufficient prediction accuracy of the trained RNN.

Suggested Citation

  • Mohsen Beigi & Hossein Beigi Harchegani & Mehdi Torki & Mohammad Kaveh & Mariusz Szymanek & Esmail Khalife & Jacek Dziwulski, 2022. "Forecasting of Power Output of a PVPS Based on Meteorological Data Using RNN Approaches," Sustainability, MDPI, vol. 14(5), pages 1-12, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:3104-:d:765785
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    References listed on IDEAS

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    1. Munir Husein & Il-Yop Chung, 2019. "Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach," Energies, MDPI, vol. 12(10), pages 1-21, May.
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    3. Beigi, Mohsen & Torki-Harchegani, Mehdi & Tohidi, Mojtaba, 2017. "Experimental and ANN modeling investigations of energy traits for rough rice drying," Energy, Elsevier, vol. 141(C), pages 2196-2205.
    4. Rodríguez, Fermín & Fleetwood, Alice & Galarza, Ainhoa & Fontán, Luis, 2018. "Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control," Renewable Energy, Elsevier, vol. 126(C), pages 855-864.
    5. Larson, David P. & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest," Renewable Energy, Elsevier, vol. 91(C), pages 11-20.
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    Cited by:

    1. Qingyuan Wang & Longnv Huang & Jiehui Huang & Qiaoan Liu & Limin Chen & Yin Liang & Peter X. Liu & Chunquan Li, 2022. "A Hybrid Generative Adversarial Network Model for Ultra Short-Term Wind Speed Prediction," Sustainability, MDPI, vol. 14(15), pages 1-16, July.
    2. Su-Chang Lim & Jun-Ho Huh & Seok-Hoon Hong & Chul-Young Park & Jong-Chan Kim, 2022. "Solar Power Forecasting Using CNN-LSTM Hybrid Model," Energies, MDPI, vol. 15(21), pages 1-17, November.

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