A research team has introduced a novel analytical framework for understanding how photovoltaic systems in buildings distribute energy across three critical pathways: local consumption, surplus export to the grid, and residual grid imports. Rather than examining only the aggregate match between solar generation and building load—the traditional approach—the new flow-oriented Dynamic Time Warping (DTW) method independently characterizes temporal relationships within each energy stream.
The study analyzed 30 full daily cycles from a university building with a 49.7 kilowatt peak rooftop solar array, using 15-minute resolution measurements. The researchers calculated DTW distances for three distinct relationships: self-consumption versus solar production, surplus export versus solar production, and grid import versus total building load. Results showed that self-consumption patterns matched solar generation most tightly, while surplus export exhibited the greatest daily variability and weakest correlation with solar output.
Critically, the findings demonstrated that conventional aggregate PV-load matching metrics obscure flow-specific information. For example, self-consumption distance showed strong inverse correlation with traditional PV-load metrics, while surplus export showed virtually no correlation. This reveals that single aggregate measures fail to capture the nuanced behavior of distributed solar systems.
The framework provides a compact, physically interpretable representation of how buildings utilize solar generation without implying causation. Cross-correlation analysis confirmed zero optimal lag across all flow pairs, validating the methodology's robustness. These diagnostic capabilities have immediate applications for distribution network operators seeking to understand solar penetration effects on specific circuits, for building energy managers optimizing battery storage dispatch, and for utilities refining rooftop solar forecasting models. The method's ability to distinguish flow-specific patterns could inform smarter grid integration strategies for distributed generation, particularly as solar deployment accelerates in urban and suburban networks.
