NDLI: MATLAB/spl reg/ implementation of neural & neuro-fuzzy approaches for short-term electricity demand forecasting

Content Provider	IEEE Xplore Digital Library
Author	Ka Fei Thang
Copyright Year	2004
Description	Author affiliation: TechSource Syst. Sdn. Bhd., Selangor, Malaysia (Ka Fei; Thang)
Abstract	Electricity is a commodity that cannot be stored in bulk; its demand must be forecasted so as to ensure adequate supply while not generating far exceeded that required. In that, forecasting the short-term demand is increasingly vital for planning operations to ensure economic margin of electricity supply. This paper demonstrates the use of MATLAB/spl reg/ technical computing tools to implement neural and neuro-fuzzy approaches for short-term demand forecasting. Two types of forecasting models are illustrated herein, i.e. one-hour ahead and next-day forecasting. Firstly, the paper emphasizes on presenting various stages of the model-development process, from preliminary data analysis to deployment of the developed models. The second section of the paper focuses on introducing the neural and neuro-fuzzy approaches. The paper concludes by illustrating the deployment aspects of the forecasting models; with the use of graphical user interfaces (GUIs) developed using MATLAB/spl reg/ to facilitate the forecasting process.
Starting Page	1213
Ending Page	1218
File Size	928352
Page Count	6
File Format	PDF
ISBN	0780386108
DOI	10.1109/ICPST.2004.1460186
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-11-21
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	MATLAB Demand forecasting Mathematical model Economic forecasting Predictive models Neural networks Fuzzy logic Power generation economics Electricity supply industry Artificial intelligence
Content Type	Text
Resource Type	Article

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