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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