The aim of the present research is to identify human subjective response (health and comfort) to the micro-thermal environment established by integration of individually controlled localized chilled beam and chilled ceiling (LCBCC) system and to compare its performance with the performance of mixing ventilation combined with chilled ceiling (CCMV).Experiments were carried out in mock-up of an office (4.1 m × 4.0 m × 3.1 m, L× W× H) with one person under two summer temperature conditions (26 °C and 28 °C). To mimic direct solar radiation in the room, five radiative panels on the wall together with electrical sheets on the half of the floor were used. The test room was set-up with two desks, as two workstations, and one laptop on each table. The main workstation (WS1) was located close to the simulated window. The second work station (WS2) was placed in the opposite side of the room. The room was equipped with two types of ventilating and cooling systems.  The first system consisted of a localized active chilled beam (LCB) unit together with chilled ceiling (CC) panels. The LCB was installed above the WS1 to create micro-environment around the occupant sitting at the desk. The supply flow rate from the LCB could be adjusted by the occupant within the range of 10 L/s to 13 L/s by means of a desk-mounted knob. The integration of mixing ventilation (MV) system and chilled ceiling panels was the second ventilating and cooling strategy. Twenty- four subjects (12 female and 12 male) were exposed to different indoor environment established by two cooling systems. Each experiment session lasted 120 min and consisted of 30 min acclimatization period and 90 min exposure period. The performance of the systems was identified and compared by physical measurements of the generated environment and the response of the human subjects. The study showed that perceived air quality (PAQ), overall thermal sensation (OTS) acceptability and local thermal sensation (LTS) acceptability clearly improved inside the micro-environment by using LCBCC system. Moreover, at the main workstation, OTS and LTS votes were close to “neutral” thermal sensation (ASHRAE seven point scale) when LCBCC system was used. However, OTS and LTS votes increased to the “slightly warm” side of the scale by applying CCMV system which implied the better cooling performance of the LCBCC system. Acceptability of work environment apparently increased under the room condition generated by LCBCC system. In agreement with human subjective study, the results from physical measurements and thermal manikin study showed that uniform thermal condition was generated all over the room. Air and operative temperature distribution was almost uniform with no difference higher than 1 °C between the measured locations in the room. Thus, both LCBCC and CCMV systems performed equally well outside of the micro-environment region. The use of the chilled ceiling had impact on the airflow interaction in the room and changed the airflow pattern. It can be concluded that the combination of convective and radiative systems can be considered as an efficient strategy to generate acceptable thermal condition in rooms.

The UK building stock accounts for about half of all energy consumed in the UK. A large portion of the energy is consumed by nondomestic buildings. Offices and retail are the most energy intensive typologies within the nondomestic building sector, typically accounting for over 50% of the nondomestic buildings’ total energy consumption. Heating, ventilating and air conditioning (HVAC) systems are the largest energy end use in the nondomestic sector, with energy consumption close to 50% of total energy consumption. Different HVAC systems have different energy requirements when responding to the same building heating and cooling demands. On the other hand, building heating and cooling demands depend on various parameters such as building fabrics, glazing ratio, building form, occupancy pattern, and many others. HVAC system energy requirements and building energy demands can be determined by mathematical modelling. A widely accepted approach among building professionals is to use building energy simulation tools such as EnergyPlus, IES, DOE2, etc. which can analyse in detail building energy consumption. However, preparing and running simulations in such tools is usually very complicated, time consuming and costly. Their complexity has been identified as the biggest obstacle. Adequate alternatives to complex building energy simulation tools are regression models which can provide results in an easier and faster way. This research deals with the development of regression models that enable the selection of HVAC systems for office buildings. In addition, the models are able to predict annual heating, cooling and auxiliary energy requirements of different HVAC systems as a function of office building heating and cooling demands. For the first part of the data set development used for the regression analysis, a data set of office building simulation archetypes was developed. The four most typical built forms (open plan sidelit, cellular sidelit, artificially lit open plan and composite sidelit cellular around artificially lit open plan built form) were coupled with five types of building fabric and three levels of glazing ratio. Furthermore, two measures of reducing solar heat gains were considered as well as implementation of daylight control. Also, building orientation was included in the analysis. In total 3840 different office buildings were then further coupled with five different HVAC systems: variable air volume system; constant air volume system; fan coil system with dedicated air; chilled ceiling system with embedded pipes, dedicated air and radiator heating; and chilled ceiling system with exposed aluminium panels, dedicated air and radiator heating. The total number of models simulated in EnergyPlus, in order to develop the input database for regression analysis, was 23,040. The results clearly indicate that it is possible to form a reliable judgement about each different HVAC system’s heating, cooling and auxiliary energy requirements based only on office building heating and cooling demands. High coefficients of determination of the proposed regression models show that HVAC system requirements can be predicted with high accuracy. The lowest coefficient of determination among cooling regression models was 0.94 in the case of the CAV system. HVAC system heating energy requirement regression models had a coefficient of determination above 0.96. The auxiliary energy requirement models had a coefficient of determination above 0.95, except in the case of chilled ceiling systems where the coefficient of determination was around 0.87. This research demonstrates that simplified regression models can be used to provide design decisions for the office building HVAC systems studied. Such models allow more rapid determination of HVAC systems energy requirements without the need for time-consuming (hence expensive) reconfigurations and runs of the simulation program.