Before diving into performance indicator, a short remark on temperature comfort is needed to
comfort approaches, formulae and standards are available: Szokolay (2004) presents a few of them ranging from the 1970’s (Humphreys, 1978; Auliciems, 1981; Griffiths, 1990; Nicol & Roaf, 1996) to end up with deDear et al. (1997). They all take into account that the comfort temperature (Tn) is a direct factor (
of the monthly mean temperature (Tm) added to a constant (C):T
n=C+T
mDifferent values were proposed in eachof the different studies. This is further elaborated by deDear & Brager (1998a) with an exhaustive literature review of all preceding works related to comfort. In one study (McCartney & Nicol, 2002) completed in five European countries, results showed that no single general formula could be applied in different countries, especially when those countries are lying north or south of Europe. On the other hand, deDear & Brager (1998b;
2000) showed that the range of comfort in a naturally ventilated space would be larger than in a space relying on mechanical cooling; the reason being that higher performance rating is expected from a mechanically cooled building versus a naturally ventilated one. Staying within similar comfort views (ASHRAE 55-2004) and as explained by Olesen & Brager (2004), although no similar formulae are given for the comfort temperature, ready-made graphs with comfort zones ranging from 80 to 90% acceptability are shown and explained, in relation to the monthly mean temperatures.
The EN-15251 European Standard (2006) and Olesen (2007) continue with the above similar equations using slightly modified values for free-running buildings. Unique values of 25.5, 26 and 27oC are given for mechanically cooled buildings with an expected acceptability of 90, 80 and 65% respectively. It’s a rather unique yet confusing contribution when it comes to its logistics or applicability; especially with the fact that comfort temperature is in direct relation with the preceding day’s mean temperatures, and no longer with the month’s. Below are the formula of (1) deDear and (2) EN-15251 for monthly comfort; they show the same overall approach, but with different variables both at ±2 oC for 90% acceptance.
T
n± 2
oC; T
n=17.8+0.31T
m(1)
T
n± 2
oC; T
n=18.8+0.33T
m(2)
Tm is the mean monthly temperature Tn is the comfort or neutral temperature
Previous sections and table 3.2 have reviewed the different performance indicators used within some of this research. This section discusses the degree hours (Dh) and the overheating hours above a benchmark temperature.
The degree hours (Dh) is defined as the summation of the number degrees on an hourly recording above or below a certain benchmark temperature (Szokolay 2008).
The comfort, discomfort or overheating is the summation of hours where temperature is beyond a set benchmark. Within different comfort approaches and its monthly variations, it is better to use a common benchmark temperature such as overheating in this case and compare the portion to overheating hours. The example in figure 3.2 used for illustration purpose only, is not based on actual data, shows the differences between those different approaches:
Degree hours will have decimals whereas the hours of overheating are integer numbers.
At day 1; the green line has the most overheating hours above 30oC at 18Dh followed by the blue line at 16 only. Whereas looking at the degree hours of the blue line is now the highest at 19.3 whereas the green is at 15.7Dh.
At day 2; both hours of overheating and degree hours correspond with both showing that the blue is performing worse.
Days 3 and 4; the green and the blue line have the same hours of overheating, yet the degree hours clearly show that the blue line is performing better than the green.
Figure 3.4 Temperature graph for illustration purposes only, showing random days with the corresponding hours of overheating above 30oC and the degree hours above 30oC
This example shows that for comparative temperature performance, using degree hours over a certain temperature is a better performance indicator since it takes into account more temperature nuances than using the hour of overheating or comfort which are likely to confuse the results.
3.9 Conclusion
Excessive mechanical cooling for summer comfort is the main problem to be addressed, especially when electricity supply is already unstable. The uncoordinated manner the numerous local bodies are dealing with this issue is further exacerbated because of lack of any solid scientific basis. This chapter sets the theoretical background of the study by defining the needed terminologies along with a review of previous research that have dealt with similar cases. The chapter starts by defining heavyweight construction and its expected temperature behaviour. It carries on with a review on heat transfers within heavy and lightweight constructions. and all the parameters that are used for quantifying thermal mass. These parameters are: time lag, decrement factor, admittance or Y-value, K-value of the capacity, and the Thermal Mass Parameter (TMP). Next the chapter reviews the EDSL TAS thermal software which is used within the research showing how it works and its different components with a detailed explanation on how each one functions, as well as the various parameters that can be inputted into the study. More importantly, the different UK and international bodies that validate its temperature and energy studies are listed. In an attempt to decipher the algorithm used for simulating heavyweight construction, the few references found are vague, varying between simplified calculation methods that take into account admittance match to the SAP 2012 guidelines. Those references discuss heat capacity and thermal mass parameters but not admittance, and their theory manuals address the specific response factors to be used to ensure that thermal mass is included in the calculations. Taking these into account, in addition to an example from their website, the chapter states that EDSL TAS is a reliable thermal software which simulates light and heavyweight temperature performance with a good level of overall accuracy. Nonetheless, when daily temperature fluctuations or differences between inside and outdoor temperatures pose an issue, the results appear to be more indicative, with smaller differences in the simulated version, as well as more minor differences between the simulated peak and the day’s peak temperature. Consequently, the process of calibration is defined and based on three verification protocols: the ASHRAE guidelines 14, the IPMVP, and the FEMP. The common points shared by the three are that the calibration is completed for energy models with either monthly or hourly figures. For the latter, the two recommended verification indices are the CVRMSE and the NMBE which should be less than 30% and 10% respectively as mentioned in ASHRAE and FEMP, whereas IPMVP proposes a lower range of 20% and 5%
respectively. A review of previous studies shows the common denominator among all the researches is their inclination for energy studies, in contrast to temperature-based studies.
Furthermore, and more critically, previous studies show that basic data of various parameters are modified to reach a good validation. Many of these wall performance researches agree that external insulation is the best location. Following the theoretical or software-based studies; the research carries on with a review on purposely built models. These actual models vary considerably in both scale and purpose; the scale can range for as small as a 300 x 300mm shoebox studied under lab conditions or as large as a 5000 x 6000 mm test cell. The larger part of these studies has focused on roof performance, with purposes varying from heat flux transfer, impact on energy loads, to temperature performance. Regarding thermal mass studies, previous researches near Cyprus and Greece (similar climate to Lebanon’s) focus on the inevitability of
adding insulation and finding the best location for it; many directives and recommendations advise lower U-values/high insulation construction. Before explaining the subtle difference between degree hours and hours of comfort or overheating, the chapter reviews the different approaches to comfort. The degree hours of overheating above 30oC is adopted for the research as the main performance indicator. The various research methods used within the previous studies can be categorised into four types: (1) solely based on actual observations, (2) software-based that could be extracted from previous observation and are either (2) calibrated or (3) not, and finally, research based on (4) purposely built models that can vary in size from small shoebox to full liveable sizes.
The problems and questions highlighted in this chapter can be summarized in the following statements:
1- Thermal software generates indicative results for heavyweight temperature simulations.
2- Calibration is possible but requires the modification of various basic parameters.
3- Conflicting results are found in similar/related studies.
4- Four types of methods are used.
The above suggest that no one method should be taken the most suitable due to a lack of consensus. Furthermore, choosing one method over the other could be a compromise; therefore, it is imperative for the research to include the four available methodologies.
Chapters 2 and 3 have defined the problem and relevant terminologies. It has also described the methodology to be followed. Lastly, it concluded following previous research that outer insulation is expected to provide the least internal overheating.