Hierarchical Short Term Load Forecasting Considering Weighting by Meteorological Region

Activities related to the planning and operation of power systems use as premise the load forecasting, which is responsible to provide a load estimative for a given horizon that assists mainly in the electroenergetic operation of a power system. The hierarchical short-term load forecasting becomes an approach used for this purpose, where the overall forecast is performed through system partition in smaller macro regions, and soon after, is aggregated to compose a global forecast. Then, this paper presents a hierarchical short-term forecasting approach for macro-regions, with the main contribution being the proposal of an indicator that represents the Average Consumption per Meteorological Region (CERM), to be used as weighting of each Meteorological Station (EM) as their importance for the total demand of the macro-region. This indicator is used to weight the temperature variable and then, is incorporated into a Multi-layer perceptron ANN model for the load forecasting on the horizon of 7 days ahead with hourly and daily discretization. The results showed higher average performance of the variable CERM in relation to the other combination performed, and the best results were used to compose the prediction of the Multi-Region (MTR). Finally, the proposed model presented a superior performance compared to an basis aggregate model for MTR, which shows the efficiency of the proposed methodology.