Accurate Disaggregation of Chiller Plant Loads Using Noisy Magnetic Field Measurements

Chiller plants consume a significant amount of energy around the world. While there have been well established systems for monitoring their performance, those existing systems are expensive and difficult to install. In this work, we propose a low-cost and non-intrusive approach to achieve accurate monitoring of individual loads in a chiller plant. Our proposed system complements the in-situ aggregated power measurement with additional magnetic field measurements near individual branches using non-intrusive Anisotropic Magnetoresistive (AMR) sensors. AMR sensor measurements help disaggregate smaller pump loads in the system. However, they are subject to various noise from the environment, which can significantly lower the estimation accuracy. To overcome this, we design a novel hybrid solution that combines the AMR-based method with a multi-layer neural network (MLNN)-based method. Specifically, we use filtered outputs (under some quality check) from the AMR-based method to help train the MLNN, and then we combine the outputs from both the AMR-based method and the MLNN-based method to achieve better estimation results. We tested our proposed solution using real world power consumption traces collected from the chiller plant of a six-story commercial building, and under varying level of environmental noise. Our approach can robustly achieve low mean-absolute error (1% for the chiller component and 3% for individual water pump loads).