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

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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.

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  • 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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