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Research on the generation method of missing hourly solar radiation data based on multiple neural network algorithm

Author

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  • Li, Honglian
  • He, Xi
  • Hu, Yao
  • Lv, Wen
  • Yang, Liu

Abstract

Solar radiation is an essential meteorological parameter for building energy efficiency analysis, and the quality of the data directly affects the analysis results. This paper investigates the estimation of hourly solar radiation based on the generation of the typical meteorological year(TMY) using various real meteorological parameters and limited solar radiation data. The focus of this paper is to use two types of neural network algorithms to improve the estimation accuracy and applicability, and to solve the problem of hourly solar radiation data acquisition in non-radiation areas. First, select two city station data and use three methods to generate TMY. Then, two neural network models, BP Neural Network (BP),Convolutional Neural Network (CNN) are used to estimate the hourly solar radiation data and verify the results. Finally, by constructing a photovoltaic-integrated office building model, the accuracy of the hourly solar radiation estimation model is verified using energy consumption simulation and photovoltaic (PV) power generation simulation. The results show that this paper can well solve the problem of limited radiation data, which provides a new idea for the study of building energy efficiency in areas where radiation data is missing.

Suggested Citation

  • Li, Honglian & He, Xi & Hu, Yao & Lv, Wen & Yang, Liu, 2024. "Research on the generation method of missing hourly solar radiation data based on multiple neural network algorithm," Energy, Elsevier, vol. 287(C).
  • Handle: RePEc:eee:energy:v:287:y:2024:i:c:s036054422303044x
    DOI: 10.1016/j.energy.2023.129650
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    References listed on IDEAS

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    1. Yang, Liu & Cao, Qimeng & Yu, Ying & Liu, Yan, 2020. "Comparison of daily diffuse radiation models in regions of China without solar radiation measurement," Energy, Elsevier, vol. 191(C).
    2. Notton, Gilles & Paoli, Christophe & Vasileva, Siyana & Nivet, Marie Laure & Canaletti, Jean-Louis & Cristofari, Christian, 2012. "Estimation of hourly global solar irradiation on tilted planes from horizontal one using artificial neural networks," Energy, Elsevier, vol. 39(1), pages 166-179.
    3. Li, Honglian & Huang, Jin & Hu, Yao & Wang, Shangyu & Liu, Jing & Yang, Liu, 2021. "A new TMY generation method based on the entropy-based TOPSIS theory for different climatic zones in China," Energy, Elsevier, vol. 231(C).
    4. Cui, Ying & Yan, Da & Hong, Tianzhen & Xiao, Chan & Luo, Xuan & Zhang, Qi, 2017. "Comparison of typical year and multiyear building simulations using a 55-year actual weather data set from China," Applied Energy, Elsevier, vol. 195(C), pages 890-904.
    5. Duan, Jikai & Zuo, Hongchao & Bai, Yulong & Chang, Mingheng & Chen, Xiangyue & Wang, Wenpeng & Ma, Lei & Chen, Bolong, 2023. "A multistep short-term solar radiation forecasting model using fully convolutional neural networks and chaotic aquila optimization combining WRF-Solar model results," Energy, Elsevier, vol. 271(C).
    6. Neshat, Mehdi & Nezhad, Meysam Majidi & Mirjalili, Seyedali & Garcia, Davide Astiaso & Dahlquist, Erik & Gandomi, Amir H., 2023. "Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy," Energy, Elsevier, vol. 278(C).
    7. Li, Honglian & Yang, Yi & Lv, Kailin & Liu, Jing & Yang, Liu, 2020. "Compare several methods of select typical meteorological year for building energy simulation in China," Energy, Elsevier, vol. 209(C).
    8. Li, Honglian & Zhang, Tiantian & Wang, An & Wang, Mengli & Huang, Jin & Hu, Yao, 2023. "A new method of generating extreme building energy year and its application," Energy, Elsevier, vol. 278(PB).
    9. Ecevit, A. & Akinoglu, B.G. & Aksoy, B., 2002. "Generation of a typical meteorological year using sunshine duration data," Energy, Elsevier, vol. 27(10), pages 947-954.
    10. Cao, Qimeng & Liu, Yan & Sun, Xue & Yang, Liu, 2022. "Country-level evaluation of solar radiation data sets using ground measurements in China," Energy, Elsevier, vol. 241(C).
    11. Moazami, Amin & Nik, Vahid M. & Carlucci, Salvatore & Geving, Stig, 2019. "Impacts of future weather data typology on building energy performance – Investigating long-term patterns of climate change and extreme weather conditions," Applied Energy, Elsevier, vol. 238(C), pages 696-720.
    12. Yang, Liu & Wan, Kevin K.W. & Li, Danny H.W. & Lam, Joseph C., 2011. "A new method to develop typical weather years in different climates for building energy use studies," Energy, Elsevier, vol. 36(10), pages 6121-6129.
    13. Peng, Jinqing & Lu, Lin & Wang, Meng, 2019. "A new model to evaluate solar spectrum impacts on the short circuit current of solar photovoltaic modules," Energy, Elsevier, vol. 169(C), pages 29-37.
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