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The relationship between energy consumption and supply is a primary factor in the planning and operation of power systems. Brazil is experiencing major problems with the energy crisis into which it was placed. The lack of investment in energy supply is one of the determining factors. During the 1980s, these investments cost $10 billion on average every year. In recent years, however, half reduced these investments. This paper proposes a method for demand forecasting based on the Singular Spectrum Analysis (SSA) and neural network. The methods are to be used by large power utility's customers and to be implemented in real-time and prevents peaks from surpassing the contracted power demand with the utility. It can be applied as an auxiliary tool for management of electrical power demand in industrial plants. The effectiveness of the method is endorsed by the high correlation between the forecasted and actual time-series forecasted.

Singular Spectrum Analysis and Neural Network to Forecast Demand in Industry