Probabilistic Disaggregation of Behind-the-Meter PV Systems Using Conformal Prediction

Abstract

Disaggregating solar photovoltaics (PV) profiles from smart electricity meter data has attracted attention, as Distribution System Operators (DSOs) need street-level PV generation profiles to improve grid operations and planning. Given the importance of reliability in operational decisions, probabilistic results are preferred to avoid overlooking potential violations. This paper proposes a probabilistic disaggregation framework based on Conformal Prediction (CP), a cutting-edge uncertainty quantification methodology. This framework trains a deterministic regressor to estimate normalized PV generation profiles and proposes an efficient capacity estimation algorithm to help compute the full PV generation profiles. To obtain probabilistic results, the framework applied CP with different variants, such as Mondrian Binning (MB) and Conformal Predictive System (CPS), to enhance the reliability of prediction intervals. To address the arbitrary bin count in CP with MB, the paper proposes a novel CP variant, namely: Adaptive Mondrian Binning (AMB). Its performance, along with other CP methods, is evaluated and benchmarked against quantile regression methods on two actual datasets from the region of Amsterdam, the Netherlands, and Sydney, Australia. Results show that using LightGBM as the deterministic regressor, AMB outperforms quantile regression and other CP variants. The generalizability of the proposed framework is analysed for both probabilistic outputs and key sub-processes, such as deterministic disaggregation and capacity estimation.