A typical single-family detached building is used for the application of the cost optimality calculation method with national factors of Jordan. Figure 2 shows the 3D rendered model for the reference building model. Table 1 summarizes the reference building specifications and characteristics, and typical load scheduling. The reference building consists of one story. The floor area is about 144 m² and the ceiling height is 2.75 m. The floor plan has been divided into several rooms that have different schedules and requirements. These rooms are occupied throughout the whole year, bedrooms during the night while the living room and the kitchen during the day. The heating thermostat setting is adjusted at 21 #x00B0;C during winter and 24 °C during the summer. All window frames are sliding aluminum, with one layer of glazing 6 mm each. The windows area constitutes 30% of the total wall area. For the cooling season, the house is served with a split unit type, on the other hand, an electric heater is being used for the heating season. The roof is flat made of cast in situ reinforced concrete. The roof consists of four layers namely; concrete plaster 50 mm, reinforced concrete 100 mm, hollow concrete block 140 mm, and concrete plaster 25 mm. The wall consists of 150 mm hollow concrete blocks are surrounded by 25 mm cement plaster. The house has been divided into several thermal zones per room with a separate thermostat that controls the heating and cooling temperature in each zone. The activity factor has been set to 0.9 as a default value to account for various occupants within the building (men, women, and children).

Figure 2

3D model of the reference building in Jordan

Three cities located in Jordan have been considered in this study, namely; Irbid, Ma'An, and Aqaba. These cities representing three distinct climatic zones of Jordan. Jordan weather is generally classified as hot and dry summer and moderately cold winter. Irbid, Jordan (latitude 31.9° North, longitude 35.9° East) is located in the northern part of Jordan. Ma'An, Jordan (latitude 30.2°, longitude 35.8°), located in the southern part of Jordan, has the highest potential for solar power generation. Aqaba, Jordan (latitude 29.5°, longitude 35.01°), has the only port in Jordan. The climate condition in Aqaba can be described as hot-humid. Some data of the climatic conditions of three selected cities in Jordan are listed in Table 3.

To determine the cost-optimal energy performance, energy efficiency technologies available in Jordan market are considered. The considered ECMs include building envelop materials and thermal mass, glazing type and size, HVAC, natural ventilation and lighting system type, and associated control settings (See Table 4). The baseline building characteristics are represented in bold.

In this study, the life cycle cost LCC is chosen as the cost function in the optimization analysis, is given as [30]:

$LCC=IC=USPW\times EC,$ (1)

where, IC is the initial cost of the applied measures, EC is annual energy cost, USPW is the uniform series present worth value that depends on both the project lifetime (N) and the discount rate (r_d), which will be given through the following relation:

$USPW=\frac{1-{(1+r_{\text{d}})}^{-n}}{r_{\text{d}}}$ (2)

The project lifetime (N) and the discount rate (r_d) are assumed to be 30 years and 5% respectively [30]. The inflation rate was set equal to be 8.9%. The local current oil price is taken to be 2.67 $/gallon [6].

Validation of building energy simulation programs is very a complex process. In practice, it is not possible to perform a complete validation of building energy simulation programs due to a large number of interlinked factors and a large number of applications and combinations. Empirical whole model validation is a highly demanding task, requires expertise in experimental design and modelling. Therefore, the empirical validation technique applied at the level of few processes is considered in this study. The occupant's behavior is highly complicated and cannot be accurately included in models, therefore, this parameter is not included in the idealized validation. To simplify the validation process, a controlled single-detached room equipped with measuring sensors is used. The test cell located in Irbid is equipped with one 1.5 ton split unit. The physical characteristic of the test cell is similar to the reference building. Cooling and lighting schedules are given and then the test cell is run for two consecutive days in summer to collect hourly data.

The impact of five ECMs on the energy consumption for one-story family has have been investigated. Table 5 lists the end-use energy distribution for the one-story family house in three different locations in Jordan. Results listed in Table 5 show that the cooling load constitutes 12% and 74.5% of the total energy consumption in Irbid and Aqaba respectively. The heating load for Irbid is 39.5%, while for Aqaba (hot environment) is 1%.

The simulation results shown in Figure 4 predicts that the total energy savings of 5% can be achieved when increasing the COP of the cooling system from 2 to 3.5.

Figure 4

Energy consumption profile for various HVAC systems, Irbid

The results listed in Table 5 demonstrate that the lighting system load ranges between 10% and 20% for the three regions studied. Lighting energy consumption profiles for three daylight control strategies are shown in Figure 5. Simulation results predict that (25-28)% of lighting energy savings can be achieved when using (linear – ON/OFF) dimming control. As mentioned previously, utilizing light control further decreases the cooling load. Results presented in Figure 5 show that an additional 0.25% of HVAC energy savings can be achieved when utilizing the daylight control strategy. It is worth mentioning that, utilizing light control leads to 4.3% saving in cooling load and a 1% increase in heat load.

Figure 5

Lighting energy consumption profiles for various lighting control, Irbid

Simulation results presented in Figure 6 show that up to 2.9% of total energy consumption can be saved by utilizing glazing with the lowest emissivity value.

Figure 6

Energy consumption profile for various glazing types, Irbid

The effect of window to wall ratio (WWR), glazing types, and orientation on the total energy consumption is illustrated in Figure 7. Simulation results predict that the total energy consumptions decrease with a larger window area. This can be explained as follow; during winter time, larger window area allows solar gain which reduces the heating load. On the other hand, a larger window area increases cooling load requirements due to excess solar gain. Simulations predict that the positive effect in winter is larger than the negative effect in summer. For all glazing types and WWR, house oriented to the south has lower energy consumption compared to those oriented to the east. Utilizing triple clear windows outperform a double low emissivity window for a traditional house. Opposite behavior was observed for two stories villa.

Figure 7

Energy consumption for various glazing area and glazing types, Irbid

The space heating and cooling load of buildings are the largest contributors to the total energy consumption. It can reach up to 70 % of the total building energy requirements [35]. Wall insulations have a significant effect on the amount of energy consumed by buildings. It has a direct effect on HVAC energy requirements. The thickness of thermal insulation is a curial factor and should be carefully investigated. Simulations are carried out to investigate the effect of the thickness of PVC material on the heating and cooling load. It should be stressed that the thermos-economic optimization of insulation thickness on walls of buildings should be further investigated in order to select the best scenario. Figure 8 illustrates the impact of the PVC insulation thickness on HVAC load for various selected locations in Jordan. These locations are Irbid, Ma'an, and Aqaba. The highest energy saving is achieved for Aqaba due to the highest HVAC load in this region. Larger wall thickness leads to higher savings.

Figure 8

HVAC saving from wall insulation for various locations

Solar radiation admitted into the occupied spaces through transparent parts of the building, have a significant impact on the visual and thermal comfort. Solar gain has a negative impact in the summer and positive impact in winter.

Three types of shadings will be used in the analysis:

1. Blinds;

2. Overhangs with 1 m projection;

3. Louvers with 1 m projection.

The following simulation results describe the impact of window shading on the cooling load in summer. As shown in Figure 9, the highest saving could be achieved when using the exterior blinds. On the other hand, louver has a negative impact on total energy consumption.

Figure 9

Total energy consumption using various shading techniques

The total energy consumption profile for various energy conservation measures is shown in Figure 10. The simulation results show that an efficient HVAC system alone achieves 5% energy saving. Lighting control saves up to 5.6%. Low emissive glazing saves only 2.9% from the base load. When implementing all the ECMs, about 13.5% savings could be achieved. Glazing and shading have a negative effect in winter as there will be less solar gain and thus higher heating load. A summary of the impact of each measure on various energy-consuming systems is described in Table 6.

Figure 10

Energy consumption under various ECMs, Irbid

Simulation results presented in Table 7 show that level one and two of the retrofit program is highly cost-effective while, level 3 is the least cost-effective for building located in the northern part of Jordan. The cooling demand is relatively low in that region, hence, replace AC units with high COP unit, is not justified in that region.

In this section, the results of the sequential search technique were summarized. First, the optimal path between the life cycle cost and the energy savings associated with installing various design options is determined. The optimum energy efficiency measures for the selected climate zones are determined. Then, the annual energy saving and life cycle cost in each climate zone are determined. Figure 11 describes the cost-optimal energy performance design path for three locations across Jordan. From the results, it could be concluded that the life cycle cost and energy savings of the optimum design options varied based on the climate zone within Jordan. The cost-optimal curve for the Irbid climate condition is nearly flat. The flat curves mean that energy performance investments are just paid back by energy savings. The optimal point is not well defined. The reason can be explained due to the weather condition which is relatively moderately hot in summer and slightly cold in winter. On the other hand, the cost-optimal point for Aqaba is well identified. It can be concluded that, under a hot climate, large energy savings can be achieved at minimum cost.

The optimum energy efficiency measures for each climate zone in Jordan are provided in Table 8. Table 9 summarized the life cycle cost of the base and optimum cases. The annual energy saving is measured relative to the base case annual energy consumption. The results presented in Table 8 indicate that one-story buildings achieve optimal source energy savings and life cycle costs with slightly different energy efficiency measures (EEM sets). The cost-optimal result of all tested combinations for hot climate can be achieved by north orientation, single clear glazing with WWR =10%, Air conditioning COP=3.5, no insulation, no overhangs, and 70% reduction in light intensity. For the building under Irbid weather conditions, the cost-optimal design is achieved with south -orientation, single clear glazing with WWR =10%, air conditioning COP=2, no overhangs, and 70% reduction in light intensity. The optimal building energy costs in Aqaba, are higher than those in other sites due to higher cooling energy use. For Aqaba region, there is no need to invest in a high-efficiency boiler since it is rarely utilized. Moreover, the use of a marginally efficient boiler is only effective only for Irbid.

The optimal building orientation is south for Irbid to help capture higher solar radiation levels to both reduce heating loads but especially to increase natural light and thus reduce lighting electricity energy use. On the other hand, the optimal building orientation is north for Aqaba to block the high level of solar radiation.

Figure 11 shows that the cost-optimal design can save up to 30% and 39% for Irbid and Aqaba building respectively. Moreover, the optimal design can reduce the life cycle costs from 12% to 34.5% for Irbid and Aqaba respectively. Moreover, the reduction in the life-cycle cost is higher in a hotter environment. The LCCs are largely due to the lower operating costs associated with the annual decrease in the consumption of electricity. For the lifespan of the building, the annual decrease in energy costs outweighs the rise in the cost of building construction to incorporate more productive features. The LCCs is lower than the baseline LCCs, all cities will achieve more than 35% of annual energy consumption savings.

Figure 11

Optimum path in various locations across Jordan

Figure 12 shows a comparison between the optimal path for Irbid obtained from the current study and the previously published work of Krarti and Ihm [30] for Damascus city. It is worth mentioning that Irbid and Damascus are neighboring cities about 200 km apart. They have almost identical weather profile. Inconsistency of financial values such as inflation rate, and interest rate, and calculation period are the main reasons for the small differences between the two studies.

Figure 12

Comparison between current study for LCC Irbid, and Damascus (at the neighboring city)

Sensitivity analysis measures the economic impact resulting from alternative values of uncertain variables that affect the economics. It shows just how sensitive the economic payoff to uncertain values of a critical input. Several parameters can alter the shape of the curve, among them geometrical building features, utilities rate, data on energy price, calculation period, discount rate, and costs. Therefore, a sensitivity analysis is performed to reduce variability within calculations. A sensitivity analysis was performed to determine the effect of the discount rates, lifetimes, energy cost, and the energy conservation measures cost to identify the most effective parameters and to validate the current methodology. The original optimization analysis is based on the discount rate 5%, and 30 years to represent the condition in Jordan.

The impact of the electricity rate is analyzed by decreasing electricity rates from 40% to 80% of the current utility rates. The effect of the reduction of electricity rates on the normalized LCC ratio is shown in Figure 13a. The current utility cost is 0.1$/kWh in Jordan. Results show that the utility rate has a significant effect on the shape of the cost – energy efficient optimal paths. In particular, the optimal energy saving is reduced from 30% to 14.8% as utility prices are reduced from the baseline rate (100%) to 20% of the original level. It was observed that energy consumption reduction initiatives are cost-effective for homeowners as long as the is no significant drop in the utility rates in the future. In particular, the optimal source energy savings is decreased from 30% to just 14.8% as the utility rates are decreased from the baseline rate (100%) to 20% of the original cost as shown in Figure 13b. This figure describes the fact that with the high subsidies in electricity costs, it is not cost-effective for the homeowners to invest in measures to reduce energy consumption. Meanwhile, the optimal normalized LCCs are decreased with the increase in utility rate. Figure 13a. illustrates the variation of the optimal normalized LCC as a function of the increased level in utility rates in Irbid.

The energy conservation measures cost is considered a key factor in design cost-efficient buildings. The effect of uncertainty of the cost of EME is investigated by varying from -40% (reduction) to 40% (increase). The results shown in Figure 13b show that the optimal normalized LCCs are slightly increased when the ECM costs are increased, while the optimal energy savings are slightly reduced. For instance, as the cost of ECM implementation rises by 40% of the current cost, the optimal normalized LCC values rise from 0.87 to 0.88 and the optimal annual savings in energy usage are marginally reduced from 29% percent to 25%. However, as the cost of ECM implementation through a rebate scheme decreases by 40%, the optimal normalized LCCs are decreased from 0.87 to 0.85 and the optimal savings in energy consumption are increased from 30% to 32%. Indeed, lower prices for EEM options contribute to additional energy-efficient characteristics that are cost-effective for homeowners to design their residential buildings, resulting in higher energy savings and lower LCC values.

The effect of discount rate is investigated by studying several values of discount rates ranging between 0.6% and 10%. Results shown in Figure 13c generally indicate that the cost-optimal calculations have low sensitivity toward changing the discount rate values. Moreover, the life cycle cost and energy savings slightly decrease with increasing discount rates. The energy-efficiency measures become more cost-effective with lower annual discount rates. For the 10% discount rate, the optimal energy saving was 29% while it was 32.5% for the 0.6% discount rate. In order to investigate the applicability of calculation periods, the effect of cost calculation period is analyzed with three alternatives: 30 years, 25 years, and 20 years. It is clear from the results presented in Figure 13d that the cost - optimal values were insensitive to the calculation period. Almost the same energy savings is observed for the three values of the calculation period. The results of sensitivity analysis are in agreement with previously published results [22], [35].

Figure 13

Sensitivity analyses. cycle cost ratio as a function of source energy savings for a residential 436 building in Doha (a) and the cost of energy-efficient measure increase for five 437 selected cities in the MENA region (b)