Details

Autor(en) / Beteiligte

• Janković, Zoran
• Selakov, Aleksandar
• Bekut, Duško
• Đorđević, Marija

Titel

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

Ist Teil von

• Electrical engineering, 2021, Vol.103 (6), p.2973-2988

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2021

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.