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Electric Load Forecasting Using Artificial Neural Networks and Deficit Management

 

作者: SAUMEN MAJUMDAR,   P. R. SHUKLA,  

 

期刊: Energy Sources  (Taylor Available online 1997)
卷期: Volume 19, issue 8  

页码: 771-782

 

ISSN:0090-8312

 

年代: 1997

 

DOI:10.1080/00908319708908889

 

出版商: Taylor & Francis Group

 

关键词: artificial neural network;optimization;short-term electric load forecasting

 

数据来源: Taylor

 

摘要:

An attempt is made to forecast electric load using neural networks. Neural networks represent a pattern or load shape and, in reality, perform a pattern recognition function. This pattern is based on training cases provided to the network. A strategy that uses a minimum distance measurement to identify the appropriate historical patterns of load and temperature readings is used to estimate the network weights. Most of the electrical power systems in India exhibit energy shortages. An optimization algorithm is used to periodically revise the forecasted load demand and the generating capacities to determine the electrical deficit.

 

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