Geotypical Growth-based Load Forecasting: An introduction to an innovative approach

摘要

An introduction to Geotypical Growth-based Load Forecasting (GGLF), long-term power distribution load forecasting based on biological concepts and segmented geographies, is presented. Using load data obtained from 165 substations in Southern Idaho and Southeastern Oregon, this document (1) describes the reasoning for using Living Systems Theory (LST) as a basis for long-term distribution load forecasting, (2) shows the relationship between the MW growth rates of the substations to their observed peak loads, (3) provides the rationale for segmenting the substations into their various geographical characteristics (geotypes), and (4) discusses a logistical regression curve-fitting model that represents the load characteristics of five example geotypes. Example geotypes discussed in the document are those common to a high plains geography, semi-arid climate type. Recommendations for additional research that applies GGLF to other climate types and to other MW load densities are also suggested.