## 2 Literature Review  
Similar to many other fields of research, AI techniques have attracted great attention in building energy consumption evaluation. Among various types of AI techniques, artificial neural network (ANN) is a well-known technique that is widely employed for many simulation problems [20; 21]. Moreover, ANN has emerged as an effective tool in building energy management [22; 23]. [PERSON] et al. [24] used linear regression models and artificial neural networks to estimate three concepts of heating and cooling energy demands, energy consumptions and CO\({}_{2}\) emissions in office buildings. They discovered that the ANN system could perform better than other models as it yields results with greater accuracy. [PERSON] et al. [25] investigated ANN capability in forecasting the diurnal cooling energy load and found that ANN can successfully predict daily energy use by taking into account the records of five previous days. In addition, [PERSON] et al. [26] conducted a comparative study that focused on the estimation of an hourly cooling load of the building. Their study compared support vector machine (SVM) against three common types of neural networks: Traditional backpropagation neural network (BPNN), general regression neural network (GRNN), and radial basis function neural network (RBFNN). They found that SVM and GRNN could offer better prediction than the RBNN and RBFNN predictive tools, based on the root mean square error (RMSE) and mean relative error (MRE). In addition to ANN, many other methods have been used in dealing with energy-efficient residential buildings problem. For instance, [PERSON] et al. [27] effectively used an indexed ARX regression model in order to forecast the thermal load of building on an hourly basis. In a case study, by [PERSON] et al. [28] examined the ability of a combined forecasting model based on the analytic hierarchy process (AHP) in appraising the hourly cooling load. They showed that combined models could outperform individual ones. In addition, some studies have investigated the efficiency of SVMs regarding the same objectives. [PERSON] et al. [29] evaluated the SVM regression method in approximating building energy consumption in case of a tropical region. Their results show that this model is reliable enough based on the calculated coefficients of variance (CV) (less than 3%) and a small percentage error (4%). [PERSON] and [PERSON] [30] modelled the dynamic cooling and heating load of an office building by developing a hybrid method based on wavelet transform, SVM, and partial least squares regression (PLS) to. Accordingly, they chose a building located in Tianjin, China. A multiple-horizon prediction was performed after they carried out a sensitivity analysis for selecting the best inputs combination. According to their results, the developed model can be used to identify an accurate perception of the problem. [PERSON] et al. [31] synthesized an adaptive neuro-fuzzy inference system (ANFIS) with a genetic algorithm in order to improve its accuracy in estimating building energy consumption. They found that this synthesis not only does provide a reliable model, but also gives better performance than does the ANN model. In addition, Random forest (RF) model has been applied for the quantitative prediction of energy performance [32]. Similarly, multiple linear regression can yield a correlation coefficient of 0.987 for evaluation of the heating energy demand [33].  
Hereafter, four general sections form the body of this research. Explanations about the considered models are available in Section 3. Section 4 is devoted to data preparation, and statistically analysis of them. Following this, the outcomes are presented and discussed in Section 5. Eventually, Section 6 is the conclusion giving a brief report of the current research.