This paper presents an artificial neural network (ANN) approach to electric energy consumption (EEC) forecasting. In order provide the forecasted energy consumption, the ANN interpolates between the EEC and its determinants in a training data set. In this study, two ANN models are presented and implemented on real EEC data. The first model is a univariate model based on past consumption values....

This paper proposes a three-step approach to forecasting time series of electricity consumption at different levels household aggregation. These are linked by hierarchical constraints—global is the sum regional consumption, for example. First, benchmark forecasts generated all using generalized additive models. Second, each series, aggregation algorithm ML-Poly, introduced Gaillard, Stoltz, and...

Accurate prediction methods are generally very computationally intensive, so they take a long time. Quick methods, on the other hand, not accurate. Is it possible to design method that is both accurate and fast? In this paper, new proposed, based so-called random time-delay patterns, named RTDP method. Using these looks for most important parts of time series’ previous evolution, uses them pred...

An increased number of intermittent renewables poses a threat to the system balance. As a result, new tools and concepts, like advanced demand-side management and smart grid technologies, are required for the demand to meet supply. There is a need for higher consumer awareness and automatic response to a shortage or surplus of electricity. The distributed water heater can be considered as one o...