- Title
- A new feature selection technique for load and price forecast of electrical power systems
- Creator
- Abedinia, Oveis; Amjady, Nima; Zareipour, Hamidreza
- Date
- 2017
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/197352
- Identifier
- vital:18835
- Identifier
-
https://doi.org/10.1109/TPWRS.2016.2556620
- Identifier
- ISSN:0885-8950
- Abstract
- Load and price forecasts are necessary for optimal operation planning in competitive electricity markets. However, most of the load and price forecast methods suffer from lack of an efficient feature selection technique with the ability of modeling the nonlinearities and interacting features of the forecast processes. In this paper, a new feature selection method is presented. An important contribution of the proposed method is modeling interaction in addition to relevancy and redundancy, based on information-theoretic criteria, for feature selection. Another main contribution of the paper is proposing a hybrid filter-wrapper approach. The filter part selects a minimum subset of the most informative features by considering relevancy, redundancy, and interaction of the candidate inputs in a coordinated manner. The wrapper part fine-tunes the settings of the composite filter.
- Publisher
- IEEE
- Relation
- IEEE transactions on power systems Vol. 32, no. 1 (2017), p. 62-74
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright IEEE
- Subject
- Electric power systems; Electrical loads; Electricity supply industry; Feature selection; Filtering algorithms; Information filters; Information theory; Interaction; Load forecast; Mathematical models; Modelling; Predictive models; Price forecast; Redundancy; Relevancy; 4008 Electrical engineering
- Reviewed
- Hits: 224
- Visitors: 202
- Downloads: 0