Optimizing Heat Pump Operation of Residential Buildings Using Calibrated R-C and Deep Learning Models and Electricity Costs Forecasts

Abstract

The aim of this research is to improve the efficiency of energy systems using the mass of the building as thermal storage. We present a case study of a residential building, in which a detailed monitoring system was installed to measure, among other parameters, the electricity consumption, the indoor air quality, and the operation of the heating system, consisting on a Heat Pump (HP) and a radiant floor. Based on the data collected, both a lumped parameter model (R-C Model) and a Deep Learning (DL) Model have been calibrated to simulate the apartment analyzed. Both models provide a significantly accurate simulation of the apartment under real operating conditions. Then, using the simulation models, different operation scenarios have been analyzed. One of the scenarios considers the thermal inertia of the apartment and the electricity costs forecast to optimize the operation of the HP. Within this scenario, energy savings up to a 35.1%, and electricity costs savings up to a 47.3%, may be achieved during a winter season, when compared to the standardized operation of the HP.