A New Feature Selection Technique for Load and Price Forecast of Electrical Power Systems

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...

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Bibliographic Details
Published inIEEE transactions on power systems Vol. 32; no. 1; pp. 62 - 74
Main Authors Abedinia, Oveis, Amjady, Nima, Zareipour, Hamidreza
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
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
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Summary: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.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2016.2556620