A data mining approach for medium-term demand forecasting

The Brazilian government, in the past, monopolized the generation, transmission and distribution of electric energy. Following world-wide tendencies, nowadays the government assumes the regulation function in a competitive and horizontal market. The variable load, fundamental in the planning of the electrical and energy operation, in the studies of magnifying and reinforcement of the basic network, assumes strategic importance in the commercial area, improving the data storage and knowledge extraction process using computational techniques. In the present work, data mining techniques are used to produce monthly load forecasts in intervals of high, medium and low consumption, according to the needs of electrical energy distribution companies. The results of neural network models, when compared to statistical results, show improved performance, presenting an Average Relative Error about 0.5% lower.