A novel approach for electrical load forecasting using distributed sensor networks

Challa, BP; Challa, S; Chakravorty, R; Deshpande, SK; Sharma, D

A novel approach for electrical load forecasting using distributed sensor networks

Challa, BP Challa, S Chakravorty, R Deshpande, SK Sharma, D

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 3rd International Conference on Intelligent Sensing and Information Processing, ICISIP 2005, 2005, pp. 189 - 194
Issue Date:: 2005-01-01

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Field	Value	Language
dc.contributor.author	Challa, BP	en_US
dc.contributor.author	Challa, S	en_US
dc.contributor.author	Chakravorty, R	en_US
dc.contributor.author	Deshpande, SK	en_US
dc.contributor.author	Sharma, D https://orcid.org/0000-0002-9421-7774	en_US
dc.date.issued	2005-01-01	en_US
dc.identifier.citation	Proceedings - 3rd International Conference on Intelligent Sensing and Information Processing, ICISIP 2005, 2005, pp. 189 - 194	en_US
dc.identifier.isbn	0780395883	en_US
dc.identifier.isbn	9780780395886	en_US
dc.identifier.uri	http://hdl.handle.net/10453/12909
dc.description.abstract	© 2005 IEEE. Electrical market often demands accurate forecasting of electrical load for planning and operation of the power infrastructure. Current models can forecast load from half hour up to 24 hours and are based on aggregate temperature for the entire day. Although these models work very well, they do not consider the intermediate real time information between time intervals to forecast load which introduce many uncertainties pertaining to factors such as climatic conditions, geographic locations etc. Furthermore, such intermediate real time information is costly and difficult to obtain. With the aid of distributed sensor networks, real time information can easily be obtained which can lead to precise planning and operation of power systems. Such information can easily improve electrical load forecasting and reduce uncertainty which can have a direct impact on the customer. We propose new and improved models for electricity load forecasting by incorporating real-time weather (temperature) information arising from the lowcost distributed sensor networks.	en_US
dc.relation.ispartof	Proceedings - 3rd International Conference on Intelligent Sensing and Information Processing, ICISIP 2005	en_US
dc.relation.isbasedon	10.1109/ICISIP.2005.1619434	en_US
dc.title	A novel approach for electrical load forecasting using distributed sensor networks	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	Bangalore, India	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2005 IEEE. Electrical market often demands accurate forecasting of electrical load for planning and operation of the power infrastructure. Current models can forecast load from half hour up to 24 hours and are based on aggregate temperature for the entire day. Although these models work very well, they do not consider the intermediate real time information between time intervals to forecast load which introduce many uncertainties pertaining to factors such as climatic conditions, geographic locations etc. Furthermore, such intermediate real time information is costly and difficult to obtain. With the aid of distributed sensor networks, real time information can easily be obtained which can lead to precise planning and operation of power systems. Such information can easily improve electrical load forecasting and reduce uncertainty which can have a direct impact on the customer. We propose new and improved models for electricity load forecasting by incorporating real-time weather (temperature) information arising from the lowcost distributed sensor networks.

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http://hdl.handle.net/10453/12909