Title :
Human-machine co-construct intelligence on horizon year load in long term spatial load forecasting
Author :
Hong, Tao ; Hsiang, Simon M. ; Xu, Le
Author_Institution :
Oper. Res., North Carolina State Univ., Raleigh, NC, USA
Abstract :
Horizon year load (HYL) is an important parameter in load forecasting algorithms that involve the Gompertz functions. Land use information has been utilized to determine HYL by computerized program. However, this approach fails when computer tries to seek optimal solution but ignores the physical meaning of the data, which can be overcome by the planners. This paper proposes and implements a methodology to determine horizon year load using land use information and planners´ domain knowledge. The proposed methodology has been implemented and applied to several US utility companies to calculate the HYL of the small areas in the service territory. The resulting HYL has been used to drive the long-term electric load growth forecasting and to get satisfying forecast.
Keywords :
artificial intelligence; load forecasting; power engineering computing; computerized program; horizon year load; human-machine co-construct intelligence; land use information; long term spatial load forecasting; long-term electric load growth forecasting; Load forecasting; Man machine systems; Meeting planning; Physics computing; Power distribution; Power generation economics; Power system economics; Power system planning; Power system reliability; Resource management; Gompertz function; greedy strategy; horizon year load; human-machine co-construct intelligence; hybrid method; long-term spatial load forecasting;
Conference_Titel :
Power & Energy Society General Meeting, 2009. PES '09. IEEE
Conference_Location :
Calgary, AB
Print_ISBN :
978-1-4244-4241-6
DOI :
10.1109/PES.2009.5275308