  This paper presents an independent component analysis (ICA) based
unsupervised-learning method for heat, ventilation, and air-conditioning (HVAC)
load disaggregation using low-resolution (e.g., 15 minutes) smart meter data.
We first demonstrate that electricity consumption profiles on mild-temperature
days can be used to estimate the non-HVAC base load on hot days. A residual
load profile can then be calculated by subtracting the mild-day load profile
from the hot-day load profile. The residual load profiles are processed using
ICA for HVAC load extraction. An optimization-based algorithm is proposed for
post-adjustment of the ICA results, considering two bounding factors for
enhancing the robustness of the ICA algorithm. First, we use the hourly HVAC
energy bounds computed based on the relationship between HVAC load and
temperature to remove unrealistic HVAC load spikes. Second, we exploit the
dependency between the daily nocturnal and diurnal loads extracted from
historical meter data to smooth the base load profile. Pecan Street data with
sub-metered HVAC data were used to test and validate the proposed
methods.Simulation results demonstrated that the proposed method is
computationally efficient and robust across multiple customers.
