Long-Term Spatial Load Forecasting Using Human-Machine Co-construct Intelligence Framework
No Thumbnail Available
Files
Date
2008-10-28
Authors
Journal Title
Series/Report No.
Journal ISSN
Volume Title
Publisher
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.
Description
Keywords
Gompertz function, human-machine co-construct intelligence, hierarchical trending method, spatial load forecasting
Citation
Degree
MS
Discipline
Operations Research