A Study on Typical Meteorological Year Generation Method for Different Energy Systems

Abstract

Typical Meteorological Year (TMY) files are essential for accurate energy performance assessment of various systems. However, existing TMY generation methods often rely on default weighting criteria, resulting in a single TMY file for a specific location, which limits their applicability in analyzing the energy performance of diverse energy systems. To address this limitation, this study proposes a novel TMY generation method tailored to different energy systems. The applicability of the proposed method is validated through case studies on three different energy systems in Beijing and Lhasa regions. The results demonstrate the effectiveness of the method in generating representative TMY data for various energy systems. The findings highlight the importance of considering the unique characteristics of each energy system and the specific purpose of TMY generation in selecting weighting factors. The developed method can facilitate more accurate energy performance analysis and decision-making in different energy systems, ultimately contributing to the promotion of sustainable energy practices.

Keywords: Typical meteorological year, Sandia method, Different energy systems, Weighting factors, Machine Learning