# A Data-Driven Game-Theoretic Approach for Behind-the-Meter PV Generation Disaggregation

arXiv: https://arxiv.org/abs/1907.06747 · HTML: https://mlsanity.com/paper/1907.06747/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [Season-Independent PV Disaggregation Using Multi-Scale Net Load Temporal Feature Extraction and Weather Factor Fusion](https://mlsanity.com/paper/2505.18747/) (2025)

## Abstract (arXiv)

Rooftop solar photovoltaic (PV) power generator is a widely used distributed energy resource (DER) in distribution systems. Currently, the majority of PVs are installed behind-the-meter (BTM), where only customers' net demand is recorded by smart meters. Disaggregating BTM PV generation from net demand is critical to utilities for enhancing grid-edge observability. In this paper, a data-driven approach is proposed for BTM PV generation disaggregation using solar and demand exemplars. First, a data clustering procedure is developed to construct a library of candidate load/solar exemplars. To handle the volatility of BTM resources, a novel game-theoretic learning process is proposed to adaptively generate optimal composite exemplars using the constructed library of candidate exemplars, through repeated evaluation of disaggregation residuals. Finally, the composite native demand and solar exemplars are employed to disaggregate solar generation from net demand using a semi-supervised source separator. The proposed methodology has been verified using real smart meter data and feeder models.