Gated Ensemble Learning Method for Demand-Side Electricity Load Forecasting

The forecasting of building electricity demand is certain to play a vital role in the future power grid. Given the deployment of intermittent renewable energy sources and the ever increasing consumption of electricity, the generation of accurate building-level electricity demand forecasts will be valuable to both grid operators and building energy management systems. The literature is rich with forecasting models for individual buildings. However, an ongoing challenge is the development of a broadly applicable method for demand forecasting across geographic locations, seasons, and use-types. This paper addresses the need for a generalizable approach to electricity demand forecasting through the formulation of an ensemble learning method that performs model validation and selection in real time using a gating function. By learning from electricity demand data streams, the method requires little knowledge of energy end-use, making it well suited for real deployments. While the ensemble method is capable of incorporating complex forecasters, such as Artificial Neural Networks or Seasonal Autoregressive Integrated Moving Average models, this work will focus on employing simpler models, such as Ordinary Least Squares and k-Nearest Neighbors. By applying our method to 32 building electricity demand data sets (8 commercial and 24 residential), we generate electricity demand forecasts with a mean absolute percent error of 7.5% and 55.8% for commercial and residential buildings, respectively.