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Multivariate Statistical Analysis based Methodology for Long-Term Demand Forecasting

Forecasting models are necessaries in electrical utilities to set the energy cover for several years in order to minimize the operational cost. In this sense, a low error is required to avoid high levels energy exchange with market and hence an increase in the operation costs. However, demand energy has monthly a behavior correlated with some economics variables (e.g., gross domestic product, gold price), type and amount of days, population, historical demand, inter alia. For this reason, it is necessary to design a statistical methodology that allows selecting suitable variables and characterize them to establish which factors are significant for understanding. This paper proposes a methodology to identify those significant factors in the monthly forecasting model for energy demand in the power purchases area. The climatic scenario to increase the sensibility of the model, when the probability of the “Niño” or “Niña” phenomena increase month to month, was proposed.