A Bayesian-based approach for the short-term forecasting of electrical loads in smart grids.: Part I: theoretical aspects

摘要

Accurate short-term load forecasting is extremely important for the optimal management of smart grids allowing the increase of energy efficiency and enabling profitable demand response strategies. However, most of industrial and domestic loads are intrinsically affected by uncertainties due to many factors such as devices operational characteristics, time of use, weather conditions and other random effects. Therefore, probabilistic load forecasting is a challenging task and in relevant literature a great interest has been recently developed towards this topic. In this paper, the uncertainties related to the load demand are modeled through different probability density functions and a probabilistic method based on the Bayesian inference and stochastic time series is proposed for the short-term forecasting of the probability density functions parameters. This paper shows the theoretical aspects of the proposed method and it is a companion paper to a paper of the same title, Part II, in which the numerical applications are reported.