An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data

TL;DR

This paper introduces an ICA-based unsupervised method for disaggregating HVAC loads from low-resolution smart meter data, using temperature-based bounds and historical load dependencies to improve accuracy and robustness.

Contribution

The paper develops a novel ICA-based approach with optimization techniques for more accurate HVAC load disaggregation from low-resolution data.

Findings

Method is computationally efficient.

Robust across multiple customers.

Effective in extracting HVAC loads.

Abstract

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.