Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks

We examine the performance of our model in a multiscale setting by considering various temporal (i.e., 15 min, hourly intervals) and spatial (i.e., all households in a region, each household) resolutions for analyzing data. Demand forecasting at the individual households' levels is a first step toward designing personalized and targeted policies for each customer. While this is a widely studied topic in digital marketing, few researches have been done in the energy sector. The results indicate that Bayesian networks can be efficiently used for probabilistic energy modeling in residential buildings by discovering the dependencies between variables.