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.
Identifer | oai:union.ndltd.org:NCSU/oai:NCSU:etd-10212008-105450 |
Date | 28 October 2008 |
Creators | Hong, Tao |
Contributors | Shu-Cherng Fang, Yahya Fathi, Simon M. Hsiang |
Publisher | NCSU |
Source Sets | North Carolina State University |
Language | English |
Detected Language | English |
Type | text |
Format | application/pdf |
Source | http://www.lib.ncsu.edu/theses/available/etd-10212008-105450/ |
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