An accurate load forecasting is always particularly important for optimal planning and energy management in smart buildings and power systems. Millions of dollars can be saved annually by increasing a small degree of improvement in prediction accuracy. However, forecasting load demand accurately is a challenging task due to multiple factors such as meteorological and exogenous variables. This paper develops a novel load forecasting model, which is based on a feed-forward artificial neural network (ANN), to predict hourly load demand for various seasons of a year. In this model, a global best particle swarm optimization (GPSO) algorithm is applied as a new training technique to enhance the performance of ANN prediction. The fitness function is defined and a weight bias encoding/decoding scheme is presented to improve network training. Influential meteorological and exogenous variables along with correlated lagged load data are also empolyed as inputs in the presented model. The data of an ISO New England grid are used to validate the performance of the developed model. The results demonstrate that the proposed forecasting model can provide significanly better forecast accuracy, training performances and convergence characteristics than contemporary techniques found in the literature. © 2016 Elsevier Ltd