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A novel uncertainty quantification method for electricity performance of building photovoltaic systems from multiple weather data sources

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  • Tan, Jingyuan
  • Tian, Wei
  • Zhong, Yidi
  • Zhang, Simeng
  • Fang, Zhenlei

Abstract

Distributed photovoltaic (PV) systems on buildings offer a promising solution for local renewable energy integration. As interest in sustainable energy grows, the demand for building PV systems is increasing. However, a significant challenge lies in accurately quantifying the uncertainty associated with building PV performance, particularly when considering multiple weather data sources. This study proposes a novel uncertainty quantification method to address this challenge. A case study of a building PV system with a PV wall and roof in Tianjin, China, is utilized to demonstrate the proposed method. Three types of weather data from different sources are considered: typical meteorological year data, multi-year historical data, and climate change projections. Uncertainty is quantified using a weighted-mean approach, incorporating weights based on recency, period, and accuracy of the data sources. Results indicate that the proposed method effectively integrates uncertainties from various data sources, providing reliable long-term energy yield estimates for PV systems. The variation coefficient of annual yield for the PV wall and roof system across ten typical meteorological year files is approximately 10 %. This study contributes to a comprehensive understanding of uncertainty in building PV performance, enabling informed decision-making in sustainable building design and energy management.

Suggested Citation

  • Tan, Jingyuan & Tian, Wei & Zhong, Yidi & Zhang, Simeng & Fang, Zhenlei, 2024. "A novel uncertainty quantification method for electricity performance of building photovoltaic systems from multiple weather data sources," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s0360544224036569
    DOI: 10.1016/j.energy.2024.133878
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    2. Gao, Qianfa & GU, Fu & Guozhu, Shenyu & Ji, Yangjian, 2026. "Optimizing hybrid microgrids based battery swapping and charging station for UAV operations in remote areas with considering multiple weather uncertainties," Applied Energy, Elsevier, vol. 404(C).

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