## 1 Introduction
In another study, [PERSON], [PERSON] [18] used six data-mining methods to predict the cooling load and HL of a building in a short-term and medium-term, including Gaussian process (GP), bagged tree (BT), ANN, boosted tree (BST), multiple linear regression (MLR), and tree bagger (TB). Ultimately, the GP model was found as the best model in their study with an RMSE of 0.782, 0.771, 2.102; R\({}^{2}\) of 0.997, 0.996, and 0.987, for 7-day, 14-day, and 1-month periods, respectively. [PERSON] et al. [19] used a reduced-order method for modeling heating and cooling energy demand with an excellent result. An error of about 0.43% for the annual loads was found in their study. By the optimization approach, [PERSON] et al. [20] developed a hybrid intelligence model, namely M5 Rules-GA, using a genetic algorithm to optimize the M5 Rules model for estimating HL of buildings using 768 experimental datasets. A variety of Ai techniques, such as GP, multi-layer perceptron (MLP), radial basis function regression (RBFR), and M5-Rules models were also developed to estimate HL. As a result, they found that their proposed M5 Rules-GA model provided the best performance with an RMSE of 0.0548 and R\({}^{2}\) of 0.998. Using another optimization algorithm, namely the evolutionary grey wolf algorithm (GWA), [PERSON], [PERSON] [21] predicted heating and cooling loads of building with high reliability. Six other AI techniques were also considered and developed to estimate the HL for their comparison purposes, including geometric semantic genetic programming (GSGP), ANN, evolutionary multivariate adaptive regression splines (EMARS), support vector regression (SVR), MLP, and random forests (RF). The same dataset (i.e., 768 experimental datasets) was used to develop the HL predictive models in their study. Their results indicated that the GWA technique can predict HL of buildings more accurately than the other models (i.e., GSGP, ANN, EMARS, SVR, MLP, and RF). Similar works for predicting HL of buildings can be found in the following literatures [22; 23; 24; 25; 26; 27; 28; 29]. Although soft computing models for estimating the HL of the building system were developed; however, they have not been evaluated comprehensively on subjective and objective factors, such as glazing area distribution (GAD), glazing area (GA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), and relative compactness (RC). Furthermore, new smart systems with high efficiency are always the target of engineers and scientists to optimize building systems, as well as smart cities planning. Therefore, this study developed and proposed a new technique to estimate HL, based on an evolutionary algorithm (particle swarm optimization--PSO) and an extreme gradient boosting (XGBoost) model, namely PSO-XGBoost model. Careful consideration of GAD, GA, O, OH, RA, WA, SA, and RC were implemented in the present study for estimating HL of building systems. Five other AI techniques include XGBoost, support vector machine (SVM), random forest (RF), Gaussian process (GP), and classification and regression trees (CART), were also investigated and developed to predict the HL of building systems and compared with the proposed PSO-XGBoost model. The structure of this study was organized as follows: Section 1 presents the reason for conducting this study and related works; Section 2 presents the details of data collection and properties of the database used; Section 3 presents the background of the methods used; Section 4 proposes the framework of the new technique to estimate the HL of buildings systems (i.e., PSO-XGBoost); Section 5 introduces several performance indices for evaluating the accuracy of the developed models; The results and discussion are presented in Section 6; finally, conclusions and remarks are given in Section 7.