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Table 1: Software and actual energy analysis.

Table 1 Software and actual energy analysis.

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Figure 1: Steady state heat transfer model of the subject flat prior to renovation.
Figure 1: Steady state heat transfer model of the subject flat prior to renovation.
Figure 2: Steady state heat transfer model of the retrofitted subject flat. Following the results obtained, Figure 2, the respective energy-saving retrofit measures mentioned above, including adjustable louvers on the south and west apertures were applied to the subject flat.
Figure 2: Steady state heat transfer model of the retrofitted subject flat. Following the results obtained, Figure 2, the respective energy-saving retrofit measures mentioned above, including adjustable louvers on the south and west apertures were applied to the subject flat.
Figure 3: Local thermal comfort zones with 90% acceptability. The pre-requisite to quantify if such energy— saving retrofit measures are effective from a thermal comfort point of view included a detailed analysis of the indoor climatic data, to check whether the temperature (T) and relative humidity (RH) readings fell within the defined TCZs. The hourly mean values of such T and RH readings need to be analysed to sum up the number of hours in the year when each specific value of T and RH occurs. Such data can then be plotted in a psychrometric chart with the number of hours (24 / day across a whole year) at each co-ordinate point [8].
Figure 3: Local thermal comfort zones with 90% acceptability. The pre-requisite to quantify if such energy— saving retrofit measures are effective from a thermal comfort point of view included a detailed analysis of the indoor climatic data, to check whether the temperature (T) and relative humidity (RH) readings fell within the defined TCZs. The hourly mean values of such T and RH readings need to be analysed to sum up the number of hours in the year when each specific value of T and RH occurs. Such data can then be plotted in a psychrometric chart with the number of hours (24 / day across a whole year) at each co-ordinate point [8].
Figure 6: Temperature comparison between the subject flat (blue), flat 5 (red) and B’Kara microclimate (green). Sn he SRSA gle eT AE Figure 6 is is a a direct temperature comparison between the subject flat’s and flat 5’s indoor temperatures, together with the B’Kara microclimate outdoor temperature, following renovations to the subject flat.
Figure 6: Temperature comparison between the subject flat (blue), flat 5 (red) and B’Kara microclimate (green). Sn he SRSA gle eT AE Figure 6 is is a a direct temperature comparison between the subject flat’s and flat 5’s indoor temperatures, together with the B’Kara microclimate outdoor temperature, following renovations to the subject flat.
Figure 4: DB 3D view of subject flat and Flat 5. This scenario was created so as to reach as much as possible a close to reality simulation including shading effects and combined thermal masses from the adjacent apartment blocks. In both software, all the relevant building fabric parameters such as U-Values, wall thicknesses and heating / cooling schedules were carefully inputted to obtain a design model as close as possible to reality.
Figure 4: DB 3D view of subject flat and Flat 5. This scenario was created so as to reach as much as possible a close to reality simulation including shading effects and combined thermal masses from the adjacent apartment blocks. In both software, all the relevant building fabric parameters such as U-Values, wall thicknesses and heating / cooling schedules were carefully inputted to obtain a design model as close as possible to reality.
Figure 5: DB 3D view of apartment blocks.
Figure 5: DB 3D view of apartment blocks.
Figure 7: Flat 5 indoor climatic data with 90% TCZs. The main scope of collecting the climatic data (T and RH) was to process it in the psychrometric charts to analyse if such energy-saving retrofit measures managed to contain the indoor climate within the TCZ limits. The measured climatic data was processed accordingly and inputted in the MET. The following plots are the results obtained — each black dot represents an average hourly reading of temperature and corresponding humidity ratio, the latter derived from the T, RH and atmospheric pressure (A P).
Figure 7: Flat 5 indoor climatic data with 90% TCZs. The main scope of collecting the climatic data (T and RH) was to process it in the psychrometric charts to analyse if such energy-saving retrofit measures managed to contain the indoor climate within the TCZ limits. The measured climatic data was processed accordingly and inputted in the MET. The following plots are the results obtained — each black dot represents an average hourly reading of temperature and corresponding humidity ratio, the latter derived from the T, RH and atmospheric pressure (A P).
Figure 8: Retrofitted Subject Flat DesginBuilder Indoor Climatic Data with 90% TCZs. Figure 7 shows that at most times, the indoor climate is by far out of the TCZs and this means that flat 5 needs a considerable amount of heating and cooling — the latter being the greater load. In addition the humidity in winter is high — this might be due to the fact that the occupants use gas heating and they keep the apartment closed due to cold temperatures. The retrofitted subject flat model was simulated via the DB software with the ISE 2005 weather data. Ideally the DB simulations had to be carried out utilising the B’Kara microclimate weather. Unfortunately this was not possible since apart from the T, RH and AP, further detailed climatic data is necessary, such as: solar incidence (albedo, all direct and diffused components), wind (speed and direction), sky visibility parameters and precipitation. The indoor climate data obtained from the DB simulations was processed via the MET obtaining the respective psychrometric chart — Figure 8.
Figure 8: Retrofitted Subject Flat DesginBuilder Indoor Climatic Data with 90% TCZs. Figure 7 shows that at most times, the indoor climate is by far out of the TCZs and this means that flat 5 needs a considerable amount of heating and cooling — the latter being the greater load. In addition the humidity in winter is high — this might be due to the fact that the occupants use gas heating and they keep the apartment closed due to cold temperatures. The retrofitted subject flat model was simulated via the DB software with the ISE 2005 weather data. Ideally the DB simulations had to be carried out utilising the B’Kara microclimate weather. Unfortunately this was not possible since apart from the T, RH and AP, further detailed climatic data is necessary, such as: solar incidence (albedo, all direct and diffused components), wind (speed and direction), sky visibility parameters and precipitation. The indoor climate data obtained from the DB simulations was processed via the MET obtaining the respective psychrometric chart — Figure 8.
Figure 9: Retrofitted subject flat indoor climatic data with 90% TCZs. Similarly the actual measured hourly indoor climatic data of the retrofitted subject flat was inputted in the MET and the respective psychrometric chart is shown in Figure 9.
Figure 9: Retrofitted subject flat indoor climatic data with 90% TCZs. Similarly the actual measured hourly indoor climatic data of the retrofitted subject flat was inputted in the MET and the respective psychrometric chart is shown in Figure 9.
Table 3: Energy-saving retrofit measures payback period.
Table 3: Energy-saving retrofit measures payback period.
Table 2: Energy—saving Retrofit measures costing, in Euros.
Table 2: Energy—saving Retrofit measures costing, in Euros.
Table 4: Future income of energy-saving retrofit measures, following the break-even point. Figures are in Euros.
Table 4: Future income of energy-saving retrofit measures, following the break-even point. Figures are in Euros.
Related topics:
Renewable EnergyEnergy and EnvironmentLow energy buildingsSmart HomesThermal comfortEnergy Efficient RetrofittingEnergy Efficiency and Low Carbon TechnologiesThermal comfort & Energy efficiency in hot climatesLow energy retrofitNet Zero Energy Homes
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