Forecasting represents a very important task for control and decision making in many fields. Forecasting the dollar price is important for global companies to plan their investments. Forecasting the wind speed for a day-ahead horizon allows dispatching clean energy efficiently. One technique developed by the artificial intelligence community that has proved to be efficient for forecasting is the probabilistic graphical models approach. In order to obtain accurate models for forecasting, there exist different assumptions that might be made. This paper presents these assumptions and the results of different experiments conducted to define the characteristics of good probabilistic graphical models. A performance comparison of several graphical models is also presented. The experiments were executed to forecast wind velocity and hence, wind power in wind farms.

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.

In this paper, we present the conceptual model of a real-world application of factored Markov Decision Processes to dam management. The idea is to demonstrate that it is possible to efficiently automate the construction of operation policies by modelling compactly the problem as a sequential decision problem that can be easily solved using stochastic dynamic programming. We will explain the problem domain and provide an analysis of the resulting value and policy functions. We will also present a useful discussion about the issues that will appear when the conceptual model to be extended into a real-world application.

This paper presents the development of a novel dynamic Bayesian network (DBN) model devoted to wind forecasting. An original procedure was developed to approximate this model, based on historical information in the form of time series. The DBN structure and parameters are learned from historical data, and this methodology can be applied to any prediction problem. In contrast to previous approaches, the proposed model considers all the relevant variables in the domain and produces a probability distribution for the predictions; providing important additional information to the decision makers. The method was evaluated experimentally with real data from a wind farm in Mexico for a time horizon of 5 hours, showing superior performance to traditional time-series prediction techniques.