Real-Time Dynamic House Thermal Model Identification for Predicting HVAC Energy Consumption

This paper presents a real-time algorithm to predict the energy consumption of the heating, ventilation, and air conditioning (HVAC) system at home. The autoregressive model with exogenous inputs (ARX model) is used to identify the house thermal model. The ARX model, with the thermostat controller, is simulated to obtain the future state of the HVAC system with the knowledge of the weather forecast data obtained from a weather server. The utility bill for the HVAC system can be estimated if a real-time price model is provided, thereafter. The proposed method is validated by experimentation in a particular home using GE Nucleus energy management system for data aggregation and algorithm implementation. The experimental results show that the energy prediction error is around 15% in both heating and cooling mode of the HVAC system.