Time-Resolved Analysis of Photovoltaic–Building Energy Matching Using Dynamic Time Warping

The increasing share of photovoltaic (PV) generation in building energy systems highlights the importance of understanding not only the magnitude but also the temporal structure of energy mismatch between PV production and building demand. This study proposes a Dynamic Time Warping (DTW)-based framework for the analysis of daily temporal mismatch patterns in a building-integrated photovoltaic system using high-resolution measurement data. Daily temporal signatures are constructed from normalized PV generation and building load profiles, allowing the analysis to focus exclusively on temporal deformation rather than absolute energy values. Pairwise DTW distances are used to construct a distance matrix that captures similarities between daily mismatch structures over an entire month. The resulting DTW distance matrix enables not only pairwise comparison of daily mismatch patterns, but also the identification of representative, transitional, and extreme days through ranking and hierarchical organization of temporal signatures. Hierarchical clustering with average linkage reveals distinct families of days characterized by similar types of temporal deformation, while a ranking based on average DTW distance provides a compact diagnostic summary of monthly variability. The findings demonstrate that PV–building energy matching is inherently time-structured, forming recurrent temporal families of days that cannot be identified using aggregate energy metrics alone. The proposed framework provides a robust diagnostic layer for time-aware energy analysis and supports the development of advanced control and management strategies that explicitly address temporal mismatch in building-integrated photovoltaic systems.