Behind-the-Meter Solar Generation Disaggregation at Varying Aggregation Levels Using Consumer Mixture Models

The increasing penetration of solar PhotoVoltaic (PV) panels in residential markets is leading to increasing solar generation hidden behind metering instruments of utility companies. Current metering infrastructure only measures the net load (sum of consumption and solar generation signals) from customers. However, it is desirable to observe solar generation separate from load consumption for grid optimizations. To enable that, we propose an unsupervised Behind-the-Meter (BTM) disaggregation model that utilizes a novel Consumer Mixture Model (CMM) for the modelling of consumption load in the disaggregation model. CMM uses consumption patterns of neighboring customers without PVs installed as features for modelling. We evaluate our model on an Australia dataset and use a load regression model and a state-of-the-art disaggregation model as baselines. We show that our model outperforms the baselines – the Mean Average Error of disaggregation results of our model was 28.37% lower than the state-of-the-art model. Additionally, we show that our model is agnostic to aggregation levels. This enables the utilities to focus on specific grid portions as needed.