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

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

Energy, 2024, vol. 313, issue C

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.

Keywords: Uncertainty quantification; Building photovoltaics; Solar irradiation; Multi-year historical data; typical weather year data (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:313:y:2024:i:c:s0360544224036569

DOI: 10.1016/j.energy.2024.133878

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