Day similarity metric model for short-term load forecasting supported by PSO and artificial neural network.

This paper proposes a new model for optimal similar days selection and its use in short-term load forecasting based on artificial neural network. Proposed day similarity metric model is based on the multi-filtering process and introduces a few novelties: (1) introduction of pre-history of similar days in a selection process; (2) extension of forecasting factors: load inertia, daylight duration and load profiles; (3) open model with possibility to add additional contribution factors; (4) particle swarm optimization is applied for calculation of the impact of different contributing factors. This approach results in optimal similar days selection even in a case where it is not obvious in advance which factors are the most relevant. Finally, the artificial neural network is used as a basic procedure for the short-term load forecast. The proposed model has been tested in the transmission system utility in Serbia, and the results are presented. [ABSTRACT FROM AUTHOR]