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Behind-the-Meter Photovoltaic Visibility from Net Smart Meters: Deployment Regimes and Operational Insights
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deep learningPV disaggregationenergy analyticsmodel adaptation
2610.12527
Builder Relevance
8h ago70%
Abstract
The paper discusses the need for visibility of behind-the-meter photovoltaic generation and evaluates deep-learning-based PV disaggregation across different deployment regimes.
Reality Card
Core Claim
The deployment regime significantly influences error variation in PV disaggregation models, more so than model choice itself.
Method / Result
The best model achieved a daylight mean absolute error (MAE) of 0.104 kW on seen households, increasing to 0.185 kW on unseen households.
Limitations
The study's findings may vary based on the specific deployment regime and the adaptation to new regions, which could limit generalizability.
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