Unifying Load Disaggregation and Prediction for Buildings With Behind-the-Meter Solar

Abstract

Real-time building-level load forecasting is important for demand response and power system planning. Behind-the-meter (BTM) solar generation in buildings is not directly measured, resulting in a lack of native load measurements, even in recorded historical data. This invisibility of native load data makes load forecasting challenging for BTM buildings. Our idea is to learn the unknown and time-varying spatial correlations of nearby buildings to enhance the overall load forecasting accuracy. To the best of our knowledge, this paper, for the first time, integrates load disaggregation and load forecasting without requiring historical native load measurements on BTM consumers. The proposed method, ULoFo, has a computationally efficient load disaggregation component and a state-of-the-art forecasting component. ULoFo also has two interaction strategies, graph sparsification, and input refurbishment, to leverage the intermediate forecasting result to enhance disaggregation accuracy, which in turn further promotes native load forecasting accuracy. ULoFo is demonstrated to outperform existing methods in practical datasets.