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Evaluation of Photovoltaic Power Generation by Using Deep Learning in Solar Panels Installed in Buildings

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  • Chih-Chiang Wei

    (Department of Marine Environmental Informatics & Center of Excellence for Ocean Engineering, National Taiwan Ocean University, No.2, Beining Rd., Jhongjheng District, Keelung City 20224, Taiwan)

Abstract

Southern Taiwan has excellent solar energy resources that remain largely unused. This study incorporated a measure that aids in providing simple and effective power generation efficiency assessments of solar panel brands in the planning stage of installing these panels on roofs. The proposed methodology can be applied to evaluate photovoltaic (PV) power generation panels installed on building rooftops in Southern Taiwan. In the first phase, this study selected panels of the BP3 series, including BP350, BP365, BP380, and BP3125, to assess their PV output efficiency. BP Solar is a manufacturer and installer of photovoltaic solar cells. This study first derived ideal PV power generation and then determined the suitable tilt angle for the PV panels leading to direct sunlight that could be acquired to increase power output by panels installed on building rooftops. The potential annual power outputs for these solar panels were calculated. Climate data of 2016 were used to estimate the annual solar power output of the BP3 series per unit area. The results indicated that BP380 was the most efficient model for power generation (183.5 KWh/m 2 -y), followed by BP3125 (182.2 KWh/m 2 -y); by contrast, BP350 was the least efficient (164.2 KWh/m 2 -y). In the second phase, to simulate meteorological uncertainty during hourly PV power generation, a surface solar radiation prediction model was developed. This study used a deep learning–based deep neural network (DNN) for predicting hourly irradiation. The simulation results of the DNN were compared with those of a backpropagation neural network (BPN) and a linear regression (LR) model. In the final phase, the panel of module BP3125 was used as an example and demonstrated the hourly PV power output prediction at different lead times on a solar panel. The results demonstrated that the proposed method is useful for evaluating the power generation efficiency of the solar panels.

Suggested Citation

  • Chih-Chiang Wei, 2019. "Evaluation of Photovoltaic Power Generation by Using Deep Learning in Solar Panels Installed in Buildings," Energies, MDPI, vol. 12(18), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:18:p:3564-:d:268112
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    References listed on IDEAS

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    Cited by:

    1. Jangwon Suh & Yonghae Jang & Yosoon Choi, 2019. "Comparison of Electric Power Output Observed and Estimated from Floating Photovoltaic Systems: A Case Study on the Hapcheon Dam, Korea," Sustainability, MDPI, vol. 12(1), pages 1-14, December.
    2. Mohamed Mohana & Abdelaziz Salah Saidi & Salem Alelyani & Mohammed J. Alshayeb & Suhail Basha & Ali Eisa Anqi, 2021. "Small-Scale Solar Photovoltaic Power Prediction for Residential Load in Saudi Arabia Using Machine Learning," Energies, MDPI, vol. 14(20), pages 1-18, October.
    3. 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).

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