April 13, 2019 0 Comment

1. Introduction The energy consumed by a building depends upon many factors. Between them we can find: weather conditions (dry bulb T) where the building is located, materials that compound the building, lightning, HVAC systems and occupancy are the most important ones. The variety of factors makes that the prediction of the consumption not easy to be carried out. Universities and companies are struggling with this issue. To predict it, both universities and companies usually develop their own simulation programmes to make assumptions and to understand better the problem they face. The most typical building subject of study are residential, engineering and office ones (Zhao and Magoulès, 2012).
Focus on the buildings sector, its final energy consumption represents over one-third of the total final energy (International Energy Agency, 2013), with the consequent CO2 emissions that are incrementing the effects of Climate Change. It is stated the in Paris Climate Change Agreement (Otto, 2016) that if we continue with this pace in terms of fossil fuel consumption, emissions derived from the buildings sector could represent, by 2050, twice as they do now. Therefore, it becomes urgent to renovate existing buildings deeply in order to improve their energy efficiency.

2. Description of the model 2.1 General Description
The building subject of simulation made on ESP-r is in Chicago. It consists of two rooms, which names are reception and office, covered by a roof. It represents a typical medical practice. We mainly focus on the reception zone when undergoing the comfort research. The façade where the reception window is located is oriented southern (towards the equator). Reception has a surface area of 48 m2 (obtained by multiplying the difference on x-y cartesian coordinates provided by ESP-r) and its window 7,5 m2 (obtained by
Figure 1: Medical Practice Building

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!

order now

multiplying the difference on x-z coordinates). Figure 1 provides the design of the building.

2.2 Thermophysical changes
The following changes are made to run comfort simulations: activation of the cooling system (the initial design does not include it), halve the size of the reception window, set a HVAC system and finally change the absorptivity and emissivity of the southern wall. Comfort parameters are analyzed then in comparison to the initial design provided by the database. Moreover, one more simulation is done to calculate how much energy can ten PVs export to the grid and how much is lost by the inverter.

3. Numerical experiments performed The simulations undertaken to analyze the thermal comfort on the medical practice are the following:
1. One winter simulation covering a week of February for each thermophysical change (and for the initial design too). This simulation also considers a heating system activated to provide warmth to the offices. The heating load, PMV and PPC comfort parameters and the energy delivered are calculated through this simulation. 2. One summer simulation covering an entire week of July for each thermophysical change. This simulation also considers the cooling system disactivated for the initial design. The cooling load, PMV and PPC comfort parameters and the energy delivered are calculated through this simulation. 3. Another yearly simulation is carried out in what 10 PVs panels are incorporated on to the south facing part of the roof, to get the year generation of them and the losses in the inverter. Note: this simulation has been done in a colleague laptop as my windows version could not match the materials needed to the roof.
4. Results and discussion All graphs shown in this section have been created with the ESPr results analysis tool. This document shows those that the author has considered as most important.

Figure 2: Heating load obtained during a winter week of the initial design

Figure 3: Cooling load obtained during a summer week. Initial design does not include cooling system

Figure 4: PMV values of the initial design, winter simulation

Figure 5: PMV values of the initial design, summer simulation

Figure 6: PMV values, summer simulation. 5000 W of cooing system with 25ºC of set point

Figure 7: PMV values, summer simulation. Half size window and 5000 W of cooing system with 25ºC of set point

Figure 8: PMV values, summer simulation. 1 ac/h infiltration from 9:00 to 18:00 during weekdays and 5000 W of cooling system with 25ºC of set point

Figure 2 and Figure 3 provide the heating load and cooling load through a winter and a summer respectively. On the one hand, heating load follows a sharply tendency which 1kW pics match with the working hours. The total heating delivered value is 46 kWh. On the other hand, it is understandable that if there is no cooling system activated, the
Figure 9: Generation of 10 PVs and Transsmission to the grid, year simulation

cooling load is zero. PMV winter and summer values (Figures 4,5) indicates a bad comfort according to ASHRAE. Without cooling system, PMV reception in summer achieves a value of 5, extremely hot. For this reason, changes are required. First of them is shown in Figure 6. Applying 5000 W of cooling system with a set point of 25ºC reduce PMV value in the reception in summer from 5 to an average of 2,5. However, the extremely unpleasant pics still appear. The energy delivered for cooling in this case is 140 kWh. Halving the window size eliminates the sharp peaks of PMV summer values. Values fluctuate between 1 and 3 but any sudden increase or decrease appears. This is mainly because less solar radiation enters during daylight hours. However, the cooling capacity applied seems to be not enough to maintain a good degree of comfort even though the window is reduced. The energy delivered for cooling in this particular study is 110 kWh. A further study with a lower set point and that size of the window would probably get better results. Figure 8 shows the case of applying an infiltration rate of 3 AC/h for the reception during working hours from Monday to Friday. Again, the PMV value got for summer in not acceptable, averaging a value of 2,3 approximately for the reception. Probably the best results would have been obtained by merging the three changes just explained. Another study has been carried out by changing the absorptivity to 0,9 and the emissivity to 0,1 of the reception south wall. It can be deduced that increasing absorptivity produce more substantial effects than decreasing the emissivity because a value of 144,5 kWh has been obtained for the energy delivered for cooling in summer. Lately, ten PV panels have been installed on the south facing part of the roof. The average generation value is approximately of 1 kW while the transmission to the grid is 700 W; so, 300 W are lost in the inverter.
Therefore, advanced improvements are required to improve the comfort of this medical practice. Going to the literature, (Balaras, 1996) investigates the role of thermal mass for reducing cooling demand while (Venkiteswaran, Liman and Alkaff, 2017) studies the cases of wall insulation by polystyrene, single low-emissivity window glazing and white painted roof for reducing cooling demand and improving thermal comfort.

5. Conclusions 1. Without cooling system, it is not possible to stay in the reception due to the extremely hot situation. 2. A 5000 W cooling capacity with an entering set point of 25ºC improves the comfort but it is still unpleasant. Reducing the set point temperature would improve the comfort but it would increase the cooling load.

3. Smaller windows reduce the cooling requirement in summer. 4. Increased absorptivity makes more difficult the heat to escape. 5. Losses in inverter in form of heat are directly related to their quality and price. It is preferable spend more money initially in a good inverter than having big losses each day.


I'm Eddie!

Would you like to get a custom essay? How about receiving a customized one?

Check it out