NDLI: A time series approach to short term load forecasting through evolutionary programming structures

Content Provider	IEEE Xplore Digital Library
Author	Chao-Ming Huang Hong-Tzer Yang
Copyright Year	1995
Description	Author affiliation: Dept. of Electr. Eng., Kao-Yuan Junior Coll. of Technol. & Commerce, Taiwan (Chao-Ming Huang)
Abstract	Multiple local minimum points often exist on the surface of forecasting error function of the time series models. Solutions of the traditional gradient search based identification technique, therefore, may stall at the local optimal points which lead to an inadequate model. By simulating natural evolutionary process, the evolutionary programming (EP) algorithm offers the capability of converging towards the global extremum of a complex error surface. The EP based load forecasting algorithm is developed to identify the autoregression moving average (ARMA) model for one week ahead hourly load demand forecasts. Numerical tests indicate the proposed EP approach provides a method to simultaneously estimate the appropriate order and parameter values of the ARMA model for diverse types of load data. Comparisons of forecasting errors are made to the traditional identification techniques used by SAS statistical commercial package.
Starting Page	583
Ending Page	588
File Size	532028
Page Count	6
File Format	PDF
ISBN	0780329813
DOI	10.1109/EMPD.1995.500792
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-11-21
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Load forecasting Genetic programming Predictive models Sociotechnical systems Synthetic aperture sonar Packaging Load modeling Parameter estimation Power system reliability Power system planning
Content Type	Text
Resource Type	Article

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