**1. Introduction**

Kuwait has experienced a steady increase in its population since the 1960s, however, with the turn of the century, an exponential rise has been observed as per Figure 1 [1]. This steep increase, along with economic growth, has resulted in higher electrical consumption, exceeding approximately 30 TWh per annum since 2000, whereas the highest level in the 1980s was less than 10 TWh [2]. Aside from the high population growth and rise in new construction, Kuwait also has a high energy use per capita, as shown in Figure 2 [3], which is mainly driven by the heavy subsidization of the cost of electricity. Having more than doubled since the early 1990s, per capita energy consumption poses a serious problem [2]. Considering the demand for labor and the fast-paced development trend in the region, both Figures 1 and 2 clearly indicate the impact created on the electrical load for Kuwait. In addition,

according to the Ministry of Energy and Water, the peak demand is expected to reach 30,000 MW by 2030, whilst 70% of this is attributed to new residential construction [4].

**Figure 1.** Population and electricity growth trends in Kuwait from 1960 to 2015 [1].

**Figure 2.** Residential energy use per capita in 2014 (kg of oil equivalent per capita) [3].

Given the growing population and new construction initiatives in the form of housing subsidies coupled with high energy consumption per capita, energy consumption growth trends create a risk for the stability of the electrical grid and meeting the national demand. While extensive studies have been published on building energy use in Kuwait, most have been observed to be geared toward the evaluation of certain policies or retrofit programs related to energy efficiency. In the literature, end-use energy consumption for residential buildings in Kuwait has been identified in studies that utilize archetypes. Baqer and Krarti [5] modeled a prototypical Kuwaiti villa and carried out a series of analyses to ascertain the effectiveness of certain energy policies, and the impact of various energy efficiency measures on energy use and peak demand. It was observed that air conditioning accounts for 72% of the total electrical usage, whereas lighting and miscellaneous household appliances account for 22% of the energy consumption combined.

Another study conducted by Krarti and Hajiah [6] examined the impact of daylight time savings (DST) on energy use for various types of buildings. Similarly, the analysis was based on a series of archetypical models that represented buildings in the residential and commercial sectors. According to their results, space cooling represents a majority of the usage and peak demand at 48% of annual energy use, and represents a peak load of 64%.

To forecast energy demand, a study by Wood and Alsayegh [7] modeled the electrical demand up to 2030 by using a top-down approach. It was developed based on historic data of oil income, gross domestic product (GDP), population, and electric load. However, a forecasting model of the energy consumption and demand by end-use using a bottom-up approach has not, to the best of our knowledge, been developed as yet. Should a breakdown of energy end-uses be analyzed and forecasted, better building energy use can be strategized as well as the development of more effective codes and standards. Given that 57% of the energy consumption is attributed to the residential sector, it is crucial to assess the baseline energy consumption patterns [6].

A number of different algorithms are available to study the residential energy consumption [8–12]. These models depend on accurate input data to generate meaningful results. Generally, the analysis methods can be divided into "top-down" and "bottom-up" approaches, as shown in Figure 3 [13,14]. The top down approach calculates the energy consumption for the entire target sector by using the econometric and technological data for the region [15,16]. On the other hand, the bottom up approach calculates the individual building energy consumption by using either statistical model or engineering models that are then aggregated to obtain the energy use for the entire sector [17,18].

**Figure 3.** Modeling techniques to estimate the residential energy consumption. Reprint with permission [14]; Copyright 2009, Elsevier.

Statistical and engineering methods represent two distinct approaches applied in the bottom-up models to determine the energy consumption of specified end-uses [14]. The statistic method first identifies a sample of households that represent the entire building stock and then uses regression and other statistic models to predict the energy use of the sampled household and hence the entire building stock [19]. Energy modeling is gaining more popularity in the bottom-up approach with the development of energy simulations. This approach utilizes the archetype models to represent the building stock and aggregate the calibrated model results to predict the energy consumptions of the entire building stock [14]. One major advantage of the energy model is that it can predict the end-use distribution without requiring sub-metering. This offers great flexibility and more detail in terms of the end-use characteristics when compared to the statistical model. However, to obtain accurate simulation results, a high-quality set of inputs often from onsite surveys and calibration to the historical energy use data are required.
