Experimental Analysis of an Air Heat Pump for Heating Service Using a “Hardware-In-The-Loop” System

Abstract

Estimating and optimizing the dynamic performance of a heat pump system coupled to a building is a paramount yet complex task, especially under intermittent conditions. This paper presents the “hardware-in-the-loop” experimental campaign of an air-source heat pump serving a typical dwelling in Pisa (Italy). The experimental apparatus uses real pieces of equipment, together with a thermal load emulator controlled by a full energy dynamic simulation of the considered building. Real weather data are continuously collected and used to run the simulation. The experimental campaign was performed from November 2019 to February 2020, measuring the system performances under real climate and load dynamics. With a water set point equal to 40 °C, the average heat pump coefficient of performance was about 3, while the overall building-plant performance was around 2. The deviation between the two performance indexes can be ascribed to the continuous on-off signals given by the zone thermostat due to the oversized capacity of the heat emission system. The overall performance raised to 2.5 thanks to a smoother operation obtained with reduced supply temperature (35 °C) and fan coil speed. The paper demonstrates the relevance of a dynamic analysis of the building-HVAC system and the potential of the “hardware-in-the-loop” approach in assessing actual part-load heat pump performances with respect to the standard stationary methodology.

Optimal Operation of Low-Capacity Heat Pump Systems for Residential Buildings through Thermal Energy Storage

“…The experimental data reported in [19] are obtained on a system without thermal storage and clearly show the performance deterioration of the heat pump under frequent part-load and on-off regimes, motivating the present study. In fact, we will evaluate the advantages and criticalities of introducing a thermal storage between the heat pump and the heat emission devices.…”

“…In the above framework, this paper moves from the experimental data collected on the performance of an air-to-water heat pump coupled with the emulated heating system of a dwelling in the mild climate of Pisa, Italy [19], analyzing the role of thermal storage tanks coupled with the heat pump to optimize the system operation and reduce the energy consumption compared to conventional direct heating.…”

“…Table 1 reports the characteristics of the installed thermal storage tank. A more detailed analysis of the system was provided by the authors of the present paper in a previous publication [19].…”

Multi-Objective Optimization of HVAC Operation for Balancing Energy Use and Occupant Comfort in Educational Buildings

“…Indeed, the implementation of the proposed optimized control in real HVAC systems may represent a critical issue, especially due to the inertia of real HVAC components, which may limit the required dynamicity of setpoints [31]. These aspects could lead to practical criticalities in the implementation phase that could be analyzed and solved with appropriate experimental analyses, to be performed either in situ or with a hardware-in-theloop apparatus emulating the building loads, as in [32]. Furthermore, in real applications, buildings' HVAC system operation will also be constrained by economic aspects, which in some cases may lead to suboptimal operation in terms of energy efficiency, as when demand-response programs are implemented for cost effectiveness.…”

Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation

“…The platform proved its efficiency in emulating the behavior of the installed PVs with a mean relative error of 0.42% and the AC load with a mean absolute error of 10 mA. Conti et al [ 47 ] showed the relevance of the dynamic coupling between an air-source heat pump and a building apartment, located in Pisa (Italy), in winter in terms of energy performances under three different operational modes. The adopted HIL extensive experimental campaign proved its potential in properly estimating the energy consumption as well as developing advanced operational strategies.…”