Baseline Estimation of Commercial Building HVAC Fan Power Using Tensor Completion

TL;DR

This paper introduces a tensor completion approach to accurately estimate HVAC fan power baselines in commercial buildings, improving demand response strategies by capturing detailed fan power patterns.

Contribution

It presents a novel application of tensor completion for HVAC fan power baseline estimation, outperforming existing methods in accuracy.

Findings

Tensor completion outperforms benchmark methods in accuracy.

The method captures complex fan power patterns effectively.

Evaluation on real building data demonstrates practical utility.

Abstract

Commercial building heating, ventilation, and air conditioning (HVAC) systems have been studied for providing ancillary services to power grids via demand response (DR). One critical issue is to estimate the counterfactual baseline power consumption that would have prevailed without DR. Baseline methods have been developed based on whole building electric load profiles. New methods are necessary to estimate the baseline power consumption of HVAC sub-components (e.g., supply and return fans), which have different characteristics compared to that of the whole building. Tensor completion can estimate the unobserved entries of multi-dimensional tensors describing complex data sets. It exploits high-dimensional data to capture granular insights into the problem. This paper proposes to use it for baselining HVAC fan power, by utilizing its capability of capturing dominant fan power patterns. The tensor completion method is evaluated using HVAC fan power data from several buildings at the University of Michigan, and compared with several existing methods. The tensor completion method generally outperforms the benchmarks.