NDLI: Spatial load forecasting based on generalized support vector machines and cellular automaton theory

Content Provider	IEEE Xplore Digital Library
Author	Li Cong Zhang Jian-hua Zhang Guo-hua Jin Cong-you Zhang Jie-chao
Copyright Year	2009
Description	Author affiliation: North China Electric Power University, Beijing, China (Li Cong) \|\| Department of Electrical Engineering, North China Electric Power University (Zhang Jian-hua)
Abstract	A method of spatial load forecasting based on support vector machines (SVM) and cellular automata (CA) is proposed in this paper. Spatial load forecasting can be regarded as a complex problem considering many kinds of factors, while the theory of CA fully reflects the idea "complex systems are from the interaction of simple subsystem”, and is an easy and effective method to deal with complicated problems. In the paper, in order to make the mode more suitable to simulate the space distribution of power load, the traditional mode of CA is advanced. Moreover, since the core definition of CA is to vary regulations, generalized SVM mode is applied to practice the conversion regulations of CA and it can effectively solve nonlinear problem and data noise question during the practice research of projects. The case in the paper proves the mode, put forward in the paper, accords with the factual situation of spatial load forecast in distribution power system.
Starting Page	1
Ending Page	6
File Size	255024
Page Count	6
File Format	PDF
ISBN	9781424449347
DOI	10.1109/SUPERGEN.2009.5348371
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Substations Urban planning Power system planning Power system stability Spatial load forecasting Electronic mail Cellular automata Support vector machines Learning systems Load forecasting Power system simulation Conversion regulations Nomenclature Automata
Content Type	Text
Resource Type	Article

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