A ground source heat pump system (GSHPS) utilizes a relatively stable underground temperature to achieve energy-saving for heating and cooling in buildings. However, continuous long-term operation will reduce the soil temperature in winter, resulting in a decline in system performance. In this research, in order to improve the system performance of a GSHPS, a ground heat pump system integrated with solar thermal storage was developed. This solar-assisted ground heat pump system (SAGHPS) can both maintain the balance of the soil temperature effectively and achieve higher system performance than the conventional system. In this paper, in order to examine the characteristics of the system, a dynamic simulation was conducted under various conditions. The results of our case study provide specific operation data such as heat exchange rate, heat source temperature, and heat pump COP. As a result, the heat pump COP of SAGHPS was 4.7%, 9.3% higher than that of the GSHPS.

To date, most of the indoor environment control is based on the dry-bulb air temperature, which is one of the simplified control methods having the limitation to truly represent the thermal comfort of individual occupants. A variety of factors affect the thermal comfort such as dry-bulb air temperature, humidity, air movement, radiation, clothing insulation, and metabolic activity level. In this circumstance, this study investigated the effects of considering hourly metabolic rate variations for predicted mean vote (PMV) control on the actual thermal load, energy usage, and life cycle cost (LCC). The case adopting PMV control taking the hourly metabolic rate into account was comparatively analyzed against the conventional dry-bulb air temperature control, using a detailed simulation technique after the validation process. As a result, when the activity state of the occupant is house cleaning in the summer, the indoor temperature decreases rapidly due to the high amount of activity. It requires a temperature that is 11.7 °C and 9.7 °C lower than the conventional dry-bulb air temperature control method, respectively, and generally forms a higher indoor air temperature than the conventional control method after 7 p.m. This means the difference in temperature to satisfy the comfort of the occupant according to the amount of activity, and during winter as opposed to summer, was found to form a lower indoor air temperature than the conventional temperature control. In case of annual boiler gas consumption, PMV control showed 7.3% less energy consumption than the dry-bulb air temperature control and showed 28.8% less energy consumption than the dry-bulb air temperature control for annual cooling electricity consumption. Considering the cooling and heating energy reduction rate and the initial installation cost of measuring equipment for real-time metabolic rate and PMV measurement, a payback period of approximately 4.15 years was required.

A ground source heat pump (GSHP) system has higher performance than air source heat pump system due to the use of more efficient ground heat source. However, the GSHP system performance depends on ground thermal properties and groundwater conditions. There are many studies on the improvement of GSHP system by developing ground heat exchanger (GHX) and heat exchange method. Several studies have suggested methods to improve heat exchange rate for the development of GHX. However, few real-scale experimental studies have quantitatively analyzed their performance using the same ground conditions. Therefore, the objective of this study was to evaluate the thermal performance of various pipe types of GHX by the thermal response test (TRT) under the same field and test conditions. Four kinds of GHX (HDPE type, HDPE-nano type, spiral fin type, and coaxial type) were constructed in the same site. Inlet and outlet temperatures of GHXs and effective thermal conductivity were measured through the TRT. In addition, the borehole thermal resistance was calculated to comparatively analyze the correlation of the heat exchange performance with each GHX. Result of the TRT revealed that averages effective thermal conductivities of HDPE type, HDPE-nano, spiral fin type, and coaxial type GHX were 2.25 W/m·K, 2.34 W/m·K, 2.55 W/m·K, and 2.16 W/m·K, respectively. In the result, it was found that the average borehole thermal resistance can be an important factor in TRT, but the effect of increased thermal conductivity of pipe material itself was not significant.

In building energy simulation, indoor thermal comfort condition, energy use and equipment size are typically calculated based on the assumption that the clothing insulation is equal to a constant value of 0.5 clo during the cooling season and 1.0 clo during the heating season. The assumption is not reflected in practice and thus it may lead to errors. In reality, occupants frequently adjust their clothing depending on the thermal conditions, as opposed to the assumption of constant clothing values above, indicating that the clothing insulation variation should be captured in building simulation software to obtain more reliable and accurate results. In this study, the impact of three newly developed dynamic clothing insulation models on the building simulation is quantitatively assessed using the detailed whole-building energy simulation program, EnergyPlus version 6.0. The results showed that when the heating ventilation and air conditioning system (HVAC) is controlled based on indoor temperature the dynamic clothing models do not affect indoor operative temperatures, energy consumption and equipment sizing. When the HVAC is controlled based on the PMV model the use of a fixed clothing insulation during the cooling (0.5 clo) and heating (1.0 clo) season leads to the incorrect estimation of the indoor operative temperatures, energy consumption and equipment sizing. The dynamic clothing models significantly (p < 0.0001) improve the ability of energy simulation tools to assess thermal comfort. The authors recommend that the dynamic clothing models should be implemented in dynamic building energy simulation software such as EnergyPlus.

This study aimed to develop a control algorithm that can operate a variable refrigerant flow (VRF) cooling system with optimal set-points for the system variables. An artificial neural network (ANN) model, which was designed to predict the cooling energy consumption for upcoming next control cycle, was embedded into the control algorithm. By comparing the predicted energy for the different set-point combinations of the control variables, the control algorithm can determine the most energy-effective set-points to optimally operate the cooling system. Two major processes were conducted in the development process. The first process was to develop the predictive control algorithm which embedded the ANN model. The second process involved performance tests of the control algorithm in terms of prediction accuracy and energy efficiency in computer simulation programs. The results revealed that the prediction accuracy between simulated and predicted outcomes proved to have a low coefficient of variation root mean square error (CVRMSE) value (10.30%). In addition, the predictive control algorithm markedly saved the cooling energy consumption by as much as 28.44%, compared to a conventional control strategy. These findings suggest that the ANN model and the control algorithm showed potential for the prediction accuracy and energy-effectiveness of VRF cooling systems.