Enhancement of distribution load modeling using statistical hybrid regression

Real-time monitoring has become a critical part of distribution network operation that enhances the control and automation capabilities as metering technologies evolve. The metering infrastructure has further extended from feeder head of a substation throughout the entire feeder loads. Despite installations of “smart” meters and related recording devices are increasing rapidly, the measurable area does not reach the ideal status that each load should be installed with a “smart” meter to constantly observe the electric information. This paper proposes a statistical approach to correlate estimated occupancy datasets of buildings with smart meter building associated with a partially observable distribution feeder. This study includes a sensitivity analysis of occupancy how it can affect load consumptions with or without the temperature load. This also creates a general load profile for other unmetered buildings within the distribution feeder where the feeder head is assumed to be observable that can be utilized to establish statistical models for unmetered buildings. A survey of occupants between buildings has been conducted to ensure consistent human movement. A statistical distribution with hybrid regression models is then formed to correlate feeder consumption with the individual building occupancy. This study is to compare statistical norms with fitting parameters that can be applied throughout all buildings with similar load profiles. The proposed method has been validated using campus metering and static occupancy datasets.