Scenario Forecasting of Residential Load Profiles

Abstract

Load forecasting is an integral part of power system operations and planning. Due to rising penetrations of rooftop PV, electric vehicles and demand response programs, forecasting the load of individual and a small group of households is becoming increasingly important. Forecasting the load accurately, however, is considerably more difficult when only a few households are included. A way to mitigate this challenge is to provide a set of scenarios instead of one point forecast, so an operator or utility can consider the full range of behaviors. This paper proposes a novel scenario forecasting approach for residential load using flow-based conditional generative models. Compared to existing scenario forecasting methods, our approach can generate scenarios that are not only able to infer possible future realizations of residential load from the observed historical data but also realistic enough to cover a wide range of behaviors. Particularly, the flow-based models utilize a flow of reversible transformations to maximize the value of conditional density function of future load given the past observations. In order to better capture the complex temporal dependency of the forecasted future load, we extend the structure design for the reversible transformations in flow-based conditional generative models by strengthening the coupling between the output and the conditioning input. The simulation results show the flow-based designs outperform existing methods in scenario forecasting for residential load by providing both more accurate and more diverse scenarios.