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AE-LSTM Based Deep Learning Model for Degradation Rate Influenced Energy Estimation of a PV System

Author

Listed:
  • Muhammad Aslam

    (Department of Electrical Engineering, Myongji University, Yongin, Gyeonggi 17058, Korea)

  • Jae-Myeong Lee

    (Department of Computer Engineering, Myongji University, Yongin, Gyeonggi 17058, Korea)

  • Mustafa Raed Altaha

    (Department of Computer Engineering, Myongji University, Yongin, Gyeonggi 17058, Korea)

  • Seung-Jae Lee

    (Department of Electrical Engineering, Myongji University, Yongin, Gyeonggi 17058, Korea)

  • Sugwon Hong

    (Department of Computer Engineering, Myongji University, Yongin, Gyeonggi 17058, Korea)

Abstract

With the increase in penetration of photovoltaics (PV) into the power system, the correct prediction of return on investment requires accurate prediction of decrease in power output over time. Degradation rates and corresponding degraded energy estimation must be known in order to predict power delivery accurately. Solar radiation plays a key role in long-term solar energy predictions. A combination of auto-encoder and long short-term memory (AE-LSTM) based deep learning approach is adopted for long-term solar radiation forecasting. First, the auto-encoder (AE) is trained for the feature extraction, and then fine-tuning with long short-term memory (LSTM) is done to get the final prediction. The input data consist of clear sky global horizontal irradiance (GHI) and historical solar radiation. After forecasting the solar radiation for three years, the corresponding degradation rate (DR) influenced energy potentials of an a-Si PV system is estimated. The estimated energy is useful economically for planning and installation of energy systems like microgrids, etc. The method of solar radiation forecasting and DR influenced energy estimation is compared with the traditional methods to show the efficiency of the proposed method.

Suggested Citation

  • Muhammad Aslam & Jae-Myeong Lee & Mustafa Raed Altaha & Seung-Jae Lee & Sugwon Hong, 2020. "AE-LSTM Based Deep Learning Model for Degradation Rate Influenced Energy Estimation of a PV System," Energies, MDPI, vol. 13(17), pages 1-14, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4373-:d:403458
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    References listed on IDEAS

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    1. Muhammad Aslam & Jae-Myeong Lee & Hyung-Seung Kim & Seung-Jae Lee & Sugwon Hong, 2019. "Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study," Energies, MDPI, vol. 13(1), pages 1-15, December.
    2. Sharma, Vikrant & Sastry, O.S. & Kumar, Arun & Bora, Birinchi & Chandel, S.S., 2014. "Degradation analysis of a-Si, (HIT) hetro-junction intrinsic thin layer silicon and m-C-Si solar photovoltaic technologies under outdoor conditions," Energy, Elsevier, vol. 72(C), pages 536-546.
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    4. Xing Zhang & Zhuoqun Wei, 2019. "A Hybrid Model Based on Principal Component Analysis, Wavelet Transform, and Extreme Learning Machine Optimized by Bat Algorithm for Daily Solar Radiation Forecasting," Sustainability, MDPI, vol. 11(15), pages 1-20, July.
    5. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
    6. Emanuele Ogliari & Francesco Grimaccia & Sonia Leva & Marco Mussetta, 2013. "Hybrid Predictive Models for Accurate Forecasting in PV Systems," Energies, MDPI, vol. 6(4), pages 1-12, April.
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    Cited by:

    1. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    2. Edna S. Solano & Carolina M. Affonso, 2023. "Solar Irradiation Forecasting Using Ensemble Voting Based on Machine Learning Algorithms," Sustainability, MDPI, vol. 15(10), pages 1-19, May.

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