## 1 Introduction  
A large portion of energy (more than 30%) is used in the building sector globally [1]. In a building, thermal energy involves two measures of cooling load (CL), and heating load (HL) [2] and these measures are regulated by heating ventilation and air conditioning (HVAC) system. The HVAC system is designed to compute the HL and CL of the space and thereby, provide a desirable indoor air condition. In this sense, some studies have focused on evaluating comfortable, yet energy-saving spaces [3]. Required cooling and heating capacities are estimated mainly according to the basic factors, including building properties, its utilization, and climate conditions. [PERSON] et al. [4] conduct research for conventional and green buildings in terms of indoor environmental quality perception. In another research, [PERSON] et al. [5] studied the expectations of the thermal and acoustic performance of wooden buildings against a heavyweight structure. Therefore, more sustainable consumption of energy can be ensured by a proper examination of the energy performance of buildings (EPB) and optimal designing of the HVAC system. Althoughmany countries take such measures, there is still a high level of energy consumption, and it is projected to increase globally. A great number of scientists believe that such an increase is because of rising living standards. For example, building construction in Europe must meet special conditions related to energy efficiency. It should be noted that 40% of energy in Europe is consumed in buildings [6]. According to reports, this value approximately comprises 39% of the total primary energy requirement (PER) and 27.5% of total national energy use in the United States [7] and China [8], respectively. According to the items mentioned above, many engineers have tried to develop different predictive and evaluative tools the primary aim in order to produce an optimal approximation of building energy consumption. So far, building energy has been modelled in a number of attempts [9; 10]. Generally, there are two common methods for EPB evaluation: Forward modelling and inverse modelling [11]. In former, the tools are used to simulate the energy performance by taking into account the actual attributes of the building (e.g., geometry). Since the application of such simulators involves control over certain parameters that are difficult to be set in practice, there are some related disadvantages. However, the chief problem in forwarding modelling is that it is not suited to appraising the EPB of the occupied buildings [12]. In addition, this method is time-consuming and requires a lot of precision as it involves a large number of parameters. Moreover, employing various simulation programs can yield estimations with varying accuracies [13]. Nonetheless, these programs could be reliable in evaluating the effects of a single parameter on EPB when other parameters remain unchanged. Designer's Simulation Toolkit (DeST) [14], Energy Plus [15], DOE-2 [16] are among the well-known computer software tools used in this subject. To assess EPB, many researchers have turned to the inverse (data-driven) modelling [17] in order to avoid the drawbacks of the simulation packages. In this sense, if a sufficient number of instances (consisting of some inputs and targets) are available, it is viable to evaluate the impact of influential factors (e.g., relative compactness, roof area, and orientation) on EPB [18; 19]. The chief advantage in using artificial intelligence (AI) is its high-performance speed, as well as the ease of implementation. Moreover, the optimal structure of machine learning methods can easily handle the changes in variables (changing in the building design parameters, in this case) that is a formidable challenge for designers and architectures alike. In what follows, an extensive report is presented in the previous attempts that investigated the feasibility of various AI techniques in EPB.