3.2.1. Air Conditioning

Space conditioning is a large driver of energy consumption in residential buildings and is affected by many variables like weather, building envelope efficiency, building size, equipment types, and occupant behaviors. Therefore, it is challenging to determine the *UEC* for AC units and some additional complexity is required for modeling space conditioning in order to obtain reasonable accuracy. This paper used archetype simulation models to estimate the average *UEC* for AC systems in the residential building stock in Kuwait. The simulation models were created in DesignBuilder, which is a user interface for the EnergyPlus simulation engine. The weather dataset used as input for the simulation models was the typical meteorological year (TMY) for Kuwait, as developed by the Kuwait Institute for Scientific Research (KISR) [33]. The TMY datasets represent one year of hourly weather data extracted from long-term data records. The data consisted of the dry-bulb temperature, diffuse radiation, direct normal radiation, wind speed, wind direction, and relative humidity, which were collected from the KISR's weather stations. Four archetype models with different thermal and equipment performance parameters were created to represent the residential building stock in Kuwait. According to the available information, and based on detailed study as part of the Kuwait-MIT (Massachusetts Institute of Technology) projects on the sustainability of Kuwait's built environment [34–36], Table 1 summarizes the archetype parameters used in the simulation. Figure 6 shows the geometry of a sample archetype model. The results from the simulation are shown in Table 2.


**Table 1.** Archetype parameters [34–36].

**Figure 6.** Screenshot of DesignBuilder software interface of a sample archetype model.


**Table 2.** Average unit energy consumption of AC systems for different residential dwellings in Kuwait.

#### 3.2.2. Water Heaters

The unit energy consumption for a water heater was estimated through Equation (9) [37]:

$$ILEC = \frac{ILage \times c\_p (T\_{supply} - T\_{tank})}{EF} \tag{9}$$

where usage is the household hot water usage in cubic meter per day; *cp* is the volumetric specific heat of water (Jm<sup>−</sup>3K−1); *Tsupply* is the incoming cold-water (C); *Ttank* is the tank temperature (C); and *EF* is the energy factor of the water heater. This was assumed to be 0.904 for standard electric water heaters and 0.95 for high efficiency ones [38]. Electricity is the only fuel used for water heating in residential buildings in Kuwait.

#### 3.2.3. Lighting

Since all electrified households use electricity for lighting, the model assumes that lighting diffusion is equal to the national electrification rate, which is almost 100% for Kuwait [1]. However, the lighting energy is largely determined by the number of lighting fixtures, type of lamps, and usage patterns. Therefore, the residential lighting stock was broken down by lamp type, based on the 2010 lighting stock data in Kuwait [39,40]. Almost 50% of the lighting stock in Kuwait is incandescent bulbs and around 37% is compact fluorescent lamps (CFL). The daily average use is estimated to be seven hours based on [30].

#### 3.2.4. Appliances

The home appliance end-use in residential households includes electric appliances like refrigerators, televisions, computers, and others. The *UEC* for appliances is the product of the nameplate wattage and the usage hours. For products with multiple modes like standby mode, energy consumption for each mode is calculated separately and added to obtain the total energy consumption in all modes. The average hour use, rated wattage, and life span for most of the appliances was estimated based on [23,29,30]. Table 3 lists the various metrics that can be used to calculate the modeled energy usage broken down by appliances.


**Table 3.** List of household appliance metrics that include corresponding power requirements, average run-time, and useful life.
