Short term energy consumption prediction using bio-inspired fuzzy systems

This paper presents a new particle swarm optimization based fuzzy inference approach to implement short term load forecasting (STLF). Fuzzy logic algorithm is widely employed for modeling highly nonlinear systems and for handling uncertainties of current control systems. However, conventional fuzzy logic systems are incapable of handling complex problems with many input variables because of fixed rule sets and fixed membership function (MF) parameters. This work therefore presents a model-based technique to optimize the fuzzy system's membership functions using particle swarm optimization (PSO) algorithm. The PSO scheme is used for identification of fuzzy models from the input-output data. The results obtained demonstrate that the developed model has better prediction capabilities than a conventional fuzzy model for the same system with heuristically defined MFs. This therefore suggests the proposed method is an effective technique for short-term load forecasting.