Day similarity metric model for short-term load forecasting supported by PSO and artificial neural network
| Autor: | Duško Bekut, Marija Đorđević, Zoran Janković, Aleksandar Selakov |
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| Rok vydání: | 2021 |
| Předmět: |
Mathematical optimization
Artificial neural network Computer science 020209 energy Applied Mathematics 020208 electrical & electronic engineering Process (computing) Particle swarm optimization 02 engineering and technology Transmission system Term (time) Similarity (network science) Metric (mathematics) 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Selection (genetic algorithm) |
| Zdroj: | Electrical Engineering. 103:2973-2988 |
| ISSN: | 1432-0487 0948-7921 |
| DOI: | 10.1007/s00202-021-01286-6 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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