Abstract
In this work we design an advanced Evolutionary Algorithm for optimizing a Kalman Filter load forecasting model. The EA employs parallel architecture and an advanced mutation operator called the "Selection Follower". Its performance is benchmarked with that of the Expectation-Maximization algorithm in minimizing the mean-square-error of the KF prediction. Results show that although the EA requires more function evaluations, it outperforms the EM algorithm consistently.
Original language | English |
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Article number | 59848 |
Pages (from-to) | 134-138 |
Number of pages | 5 |
Journal | IEE Conference Publication |
Issue number | 478 I |
Publication status | Published - Nov 2000 |
Event | 5th International Conference on Advances in Power System Control, Operation and Management - Tsimshatsui, Kowloon, Hong Kong Duration: 30 Oct 2000 → 1 Nov 2000 |
Keywords
- Adaptive mutation
- Evolutionary algorithm
- Genetic algorithm
- Kalman filter
- Load forecasting
ASJC Scopus subject areas
- Electrical and Electronic Engineering