Neural network model ensembles for building-level electricity load forecasts

Abstract

The future power grid is expected to provide unprecedented flexibility in how energy is generated, distributed, and managed, which increasingly necessitates an ability to perform accurate short-term small-scale electricity load and generation forecasting, e.g., at the level of individual buildings or sites. In this paper, we present a novel building-level neural network-based ensemble model for day-ahead electricity load forecasting and show that it outperforms the previously established best performing model, SARIMA, by up to 50%, in the context of load data from half a dozen operational commercial and industrial sites. In addition, we show a straightforward, automated way to select model parameters, making our model practical for use in real deployments.