NDLI: Development of an intelligent long-term electric load forecasting system

Content Provider	IEEE Xplore Digital Library
Author	Parlos, A.G. Oufi, E. Muthusami, J. Patton, A.D. Atiya, A.F.
Copyright Year	1996
Description	Author affiliation: Texas A&M Univ., College Station, TX, USA (Parlos, A.G.; Oufi, E.; Muthusami, J.; Patton, A.D.)
Abstract	An essential element of electric utility resource planning is forecasting of the future load demand in the service area. Based on the outcome of such forecasts, utilities coordinate their resources to meet the forecasted demand using a least-cost plan. In general, resource planning is performed subject to numerous uncertainties. Expert opinion indicates that a major source of uncertainty in planning for future capacity resource needs and operation of existing generation resources is the forecasted load demand. In this paper, the development and testing of a hybrid intelligent long-term load forecasting system is presented consisting of several neural networks forecasting blocks, genetic algorithms for network architecture selection and optimization, and fuzzy rules for forecast combination. This is an application of increasingly significant importance to a deregulating electric utility industry. An overview of the current practice in long term load forecasting is presented, followed by an overview of the forecasting system design process utilized in generating the long-term load forecasts and a brief description of the key building blocks of the forecasting system. This is followed by some sample long-term forecasts performed for demonstrating the feasibility of the proposed approach.
Starting Page	288
Ending Page	292
File Size	589148
Page Count	5
File Format	PDF
ISBN	078033115X
DOI	10.1109/ISAP.1996.501084
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-01-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	System testing Intelligent networks Uncertainty Load forecasting Neural networks Demand forecasting Fuzzy neural networks Power industry Capacity planning Genetic algorithms
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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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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