A tool for learning dynamic bayesian networks for forecasting

Abstract

Renewable energy is increasing its participation in power generation in many countries. In Mexico, the strategy is to generate 35% of electricity from renewable sources by 2024. Currently only 18.3% of the generated energy is obtained from renewable and clean sources. The integration of renewable energies in the energy market is a challenge due to their high variability, instability and uncertainty. Hence, energy forecast is the required service by the power generators to offer energy with certain degree of confidence. Dynamic Bayesian networks (DBNs) have proved to be an appropriate mechanism for uncertainty and time reasoning; however there is no basic tool that builds DBN using time series for a process. This paper describes the design, construction and tests for a DBNs learning tool. This tool has already been used to construct dynamic models for wind power forecast and in this paper it is used to describe the variation of the dam level caused by rainfall in a hydroelectric power plant.