NDLI: Electricity Price Forecasting Using Evolved Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Srinivasan, D. Fen Chao Yong Ah Choy Liew
Copyright Year	2007
Description	Author affiliation: Nat. Univ. of Singapore, Singapore (Srinivasan, D.; Fen Chao Yong; Ah Choy Liew)
Abstract	Evolutionary techniques have capabilities of efficient search space exploration with population models corresponding to the problem. Their ability to capture the non linear dependencies among the system variables has invited economic analysts towards their use in the field of financial time series prediction. Although simple neural networks with sufficient number neuron units in the hidden layer are capable of following dynamics of any deterministic system, the weight search space becomes too complex to be searched using a simple back propagation based training algorithm. This paper presents and evaluates two alternative methods for finding the optimum weights of a neural network to capture the chaotic dynamics of electricity price data. The first method uses evolutionary algorithm to evolve a neural network, and the second method uses particle swarm optimization for NN training. The global search capabilities of these evolutionary methods is used for finding the optimum neural network for forecasting electricity price from the California Power Exchange.
Starting Page	1
Ending Page	7
File Size	535604
Page Count	7
File Format	PDF
ISBN	9789860126075
DOI	10.1109/ISAP.2007.4441660
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-05
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	ISAP
Subject Keyword	Chaos Neural networks Time series analysis Neurons Power generation economics Evolutionary computation Economic forecasting Power markets Space exploration Particle swarm optimization
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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