Long-Term Spatial Load Forecasting Using Human-Machine Co-construct Intelligence Framework

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dc.contributor.advisor Shu-Cherng Fang, Committee Member en_US
dc.contributor.advisor Yahya Fathi, Committee Member en_US
dc.contributor.advisor Simon M. Hsiang, Committee Chair en_US
dc.contributor.author Hong, Tao en_US
dc.date.accessioned 2010-04-02T17:53:41Z
dc.date.available 2010-04-02T17:53:41Z
dc.date.issued 2008-10-28 en_US
dc.identifier.other etd-10212008-105450 en_US
dc.identifier.uri http://www.lib.ncsu.edu/resolver/1840.16/178
dc.description.abstract This thesis presents a formal study of the long-term spatial load forecasting problem: given small area based electric load history of the service territory, current and future land use information, return forecast load of the next 20 years. A hierarchical S-curve trending method is developed to conduct the basic forecast. Due to uncertainties of the electric load data, the results from the computerized program may conflict with the nature of the load growth. Sometimes, the computerized program is not aware of the local development because the land use data lacks such information. A human-machine co-construct intelligence framework is proposed to improve the robustness and reasonability of the purely computerized load forecasting program. The proposed algorithm has been implemented and applied to several utility companies to forecast the long-term electric load growth in the service territory and to get satisfying results. en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dis sertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.subject Gompertz function en_US
dc.subject human-machine co-construct intelligence en_US
dc.subject hierarchical trending method en_US
dc.subject spatial load forecasting en_US
dc.title Long-Term Spatial Load Forecasting Using Human-Machine Co-construct Intelligence Framework en_US
dc.degree.name MS en_US
dc.degree.level thesis en_US
dc.degree.discipline Operations Research en_US

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