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. (C) 2014 Elsevier B.V. All rights reserved.