Forecasting of Residential Power Consumer Load Profiles Using a Type-2 Fuzzy Inference System

The key focus of this article is forecasting of residential power consumer load profiles using tuned Type-2 Fuzzy Inference System. The characteristics of residential load profiles have been investigated. In contrast to similar studies, non-averaged profiles with one minute resolution have been used. Additionally, the presence of various shapes in these profiles increases the difficulty of forecasting. In this paper, the process of creating, learning and tuning Type-2 Fuzzy Inference System with Particle Swarm Optimization and Genetic Algorithm is presented. The accuracy of the forecasts was evaluated using Root-Mean-Square Error calculations. The obtained results showed that the proposed method can predict detailed load profiles efficiently. The biggest forecast error was 0.1165, while the lowest was 0.0642. Additionally, the value of error was influenced also by the type of day (working day or Saturday). Moreover, the Particle Swarm Optimization proved to be a more precise tuning solution than the Genetic Algorithm, obtaining lower error values. Several aspects related to the residential load profiles forecasting are also discussed in this paper. The presented research may be useful for companies selling electricity.