Solar Power Forecasting Using CNN-LSTM Hybrid Model

Su-Chang Lim, Jun-Ho Huh, Seok-Hoon Hong, Chul-Young Park () and Jong-Chan Kim ()
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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

Energies, 2022, vol. 15, issue 21, 1-17

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.

Keywords: PV system; PV power generation forecasting; AI; deep learning; CNN; LSTM network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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