Human-machine co-construct intelligence on horizon year load in long term spatial load forecasting

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.