An Adaptive Machine Learning Framework for Behind-the-Meter Load/PV Disaggregation

A significant amount of distributed photovoltaic (PV) generation is invisible to distribution system operators since it is behind the meter on customer premises and not directly monitored by the utility. The generation essentially adds an unknown varying negative demand to the system, which causes additional uncertainty in determining the total load. This uncertainty directly impacts system reliability, cold load pickup, load behavior modeling, and hence cost of operation. This paper proposes an adaptive machine learning framework to disaggregate PV generation and load from the net measurement. The framework estimates PV generation and load based on measurements/data collected from smart transformers, smart meters, other sensors, and weather stations. The proposed framework core idea is to transform the data such that: a) the machine learning model can effectively utilize the time dependency of measurements, and b) the measurements are transformed into a lower-dimensional space to reduce complexity while maintaining accuracy. The transformed measurements are then used to train the machine learning models for load/PV dis-aggregation. Machine learning models investigated include linear regression (LR), decision tree (DT), random forest (RF), and Multilayer Perceptron (MLP). Several test/ training split scenarios, including 90%-10% Split, One-Month-Out, One-Season-Out, and Panel-Independent Split, are presented to provide a thorough evaluation of the proposed framework. Results show that the proposed framework can estimate PV generation with high accuracy using low-complexity methods, and random forest is found to provide superior performance compared to the other ML models investigated.