Physics-Infused Retrofit Isolation Measurement and Verification for AI-Driven Energy Application

Amir Behjat, Gulai Shen, Gurpreet Singh, Gerald Toge, Ali Mehmani

Research and Development, Nantum AI

Tracking energy efficiency improvements, meeting environmental targets, and promoting sustainability practices are possible through accurate measurement and verification. In this paper, we offer a retrofit isolation measure and verification technology founded on the IPMVP standard of measuring the impact of different AI-driven ECMs, anomaly detection, and notification services. This technology uses physics-infused machine-learning models that are constructed using static and dynamic data. The key attributes of this scalable and open-source technology lie not only in its ability to provide accurate measurements and validations of the target ECMs, but also in its robustness to common non-routine adjustments such as changes in occupancy, alterations in facility size (e.g., added square footage), variations in space type or usage, increase in energy demand(e.g., new IT centers, additional plug loads, and new data centers), as well as adjustments in zone temperature set points.