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Day similarity metric model for short-term load forecasting supported by PSO and artificial neural network.
- Source :
- Electrical Engineering; Dec2021, Vol. 103 Issue 6, p2973-2988, 16p
- Publication Year :
- 2021
-
Abstract
- 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]
Details
- Language :
- English
- ISSN :
- 09487921
- Volume :
- 103
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- Electrical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 153652714
- Full Text :
- https://doi.org/10.1007/s00202-021-01286-6