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Solar Power Forecasting Using CNN-LSTM Hybrid Model

Author

Listed:
  • Su-Chang Lim

    (R&D Center, TEF Co., Ltd., 60-12 Suncheon-ro, Seo-Myeon, Suncheon 57906, Korea)

  • Jun-Ho Huh

    (Department of Data Science, Korea Maritime and Ocean University, Busan 49112, Korea)

  • Seok-Hoon Hong

    (R&D Center, TEF Co., Ltd., 60-12 Suncheon-ro, Seo-Myeon, Suncheon 57906, Korea)

  • Chul-Young Park

    (R&D Center, TEF Co., Ltd., 60-12 Suncheon-ro, Seo-Myeon, Suncheon 57906, Korea)

  • Jong-Chan Kim

    (Department of Computer Engineering, Sunchon National University, Suncheon 57992, Korea)

Abstract

Photovoltaic (PV) technology converts solar energy into electrical energy, and the PV industry is an essential renewable energy industry. However, the amount of power generated through PV systems is closely related to unpredictable and uncontrollable environmental factors such as solar radiation, temperature, humidity, cloud cover, and wind speed. Particularly, changes in temperature and solar radiation can substantially affect power generation, causing a sudden surplus or reduction in the power output. Nevertheless, accurately predicting the energy produced by PV power generation systems is crucial. This paper proposes a hybrid model comprising a convolutional neural network (CNN) and long short-term memory (LSTM) for stable power generation forecasting. The CNN classifies weather conditions, while the LSTM learns power generation patterns based on the weather conditions. The proposed model was trained and tested using the PV power output data from a power plant in Busan, Korea. Quantitative and qualitative evaluations were performed to verify the performance of the model. The proposed model achieved a mean absolute percentage error of 4.58 on a sunny day and 7.06 on a cloudy day in the quantitative evaluation. The experimental results suggest that precise power generation forecasting is possible using the proposed model according to instantaneous changes in power generation patterns. Moreover, the proposed model can help optimize PV power plant operations.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8233-:d:963128
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    References listed on IDEAS

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    2. Lioua Kolsi & Sameer Al-Dahidi & Souad Kamel & Walid Aich & Sahbi Boubaker & Nidhal Ben Khedher, 2022. "Prediction of Solar Energy Yield Based on Artificial Intelligence Techniques for the Ha’il Region, Saudi Arabia," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    3. Elias Roumpakias & Tassos Stamatelos, 2023. "Comparative Performance Analysis of a Grid-Connected Photovoltaic Plant in Central Greece after Several Years of Operation Using Neural Networks," Sustainability, MDPI, vol. 15(10), pages 1-26, May.
    4. Izabela Rojek & Dariusz Mikołajewski & Adam Mroziński & Marek Macko, 2023. "Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage," Energies, MDPI, vol. 16(18), pages 1-26, September.

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