Introduction

A short-term load forecast is the prediction of the consumption of resources in a distribution network in the near future. The supplied resource can be of any kind, such as electricity in power grids or telephone service in telecommunication networks. An accurate forecast of the demand is of utmost importance for the planning of facilities, optimization of day-to-day operations, and an effective management of the available resources. In the context of energy and telecommunication networks, the load data are usually represented as real-valued time series characterized by strong temporal dependencies and seasonal patterns. We begin by reviewing several methods that have been adopted in the past years for the task of short-term load forecast and we highlight their main advantages and limitations. We then introduce the framework of recurrent neural networks, a particular class of artificial neural networks specialized in the processing of sequential/temporal data. We explain how recurrent neural networks can be an effective tool for prediction, especially in those cases where the extent of the time dependencies is unknown a-priori.