PREDICT – Decision support system for load forecasting and inference: A new undertaking for Brazilian power suppliers

One of the most desired aspects for power suppliers is the acquisition/sale of energy for a future demand. However, power consumption forecast is characterized not only by the variables of the power system itself, but also related to social–economic and climatic factors. Hence, it is imperative for the power suppliers to project and correlate these parameters. This paper presents a study of power load forecast for power suppliers, considering the applicability of wavelets, time series analysis methods and artificial neural networks, for both mid and long term forecasts. Both the periods of forecast are of major importance for power suppliers to define the future power consumption of a given region. The paper also studies the establishment of correlations among the variables using Bayesian networks. The results obtained are much more effective when compared to those projected by the power suppliers based on specialist information. The research discussed here is implemented on a decision support system, contributing to the decision making for acquisition/sale of energy at a future demand; also providing them with new ways for inference and analyses with the correlation model presented here.

► We implement a novel decision support system for Brazilian power suppliers. ► Statistical and computational intelligence models are evaluated for the domain. ► A hybrid load forecasting framework proved greater accuracy to the time series. ► Correlation studies are implemented for improving decision support of managers. ► Results allow for better estimating consumption and evaluate its impacting factors.