Abstract
In the new global economy, energy consumption has become a major issue for residential and commercial buildings. This paper discusses the accuracy of load forecasting for the aggregated load for residential and commercial buildings in the United States. The literature presents a plethora of different consumption forecasting approaches that vary in concept from conventional mathematical stochastic methods to the use of artificial neural network (ANN). The literature shows that ANN have many advantages over conventional techniques, hence the efficacy of ANN models to forecast energy consumptions are investigated. The objective of this paper is to compare recurrent ANN models with non-recurrent ANN models. The recurrent ANNs models used are the Long Short-Term Memory and Gated Recurrent Unit. The non-recursive ANNs studies are the Radial Basis Function Network and Multilayer Perceptron. The parameters examined are short and medium-term load forecasting. The study was made on historic data collected for individual buildings.