## 1 Introduction  
One of the indispensable components for smart cities is energy and the applications of artificial intelligence (AI) [1]. Nowadays, smart cities are becoming more popular and the first choice for those who want a comfortable and productive life [2; 3; 4; 5]. This includes intelligent, modern, energy efficient utilities, as well as sustainable environmental protection [6; 7; 8]. Of those components, heating load (HL) and cooling load (CL) systems are a part of energy efficiency. Many studies were conducted to predict and optimize the use of buildings' energy efficiency (EEB) as well as building energy consumption [9]. For instance, [PERSON] et al. [10] used multiple regression method to estimate the demand for heating energy of the building. The south equivalent surface, global heat loss coefficient of building, and the difference between the sol-air and the indoor temperatures, were used as the input variables to estimate the demand of heating energy in their study. Their positive results were confirmed with a determination coefficient (R\({}^{2}\)) of 0.987. [PERSON], [PERSON] [11] also developed an ensemble model based on support vector regression (SVR) and an artificial neural network (ANN) to predict HL and CL for building design, called ANN-SVR, using the datasets of 17 buildings. A variety of the other models were also considered and developed to investigate and compare with their proposed ANN-SVR model, including SVR, ANN, chi-squared automatic interaction detector, classification and regression tree, and general linear regression. Their results confirmed the feasibility of AI techniques designing and optimizing EEB systems, especially the ANN-SVR model with a mean absolute percentage error (MAPE) below 4% and root-mean-squared error (RMSE) lower than 39%-65.9% in a comparison of the previous works [12; 13]. In another study, [PERSON] et al. [14] applied a genetic programming (GP) model for evaluating the energy efficiency of EEB systems. Three forms of GP were investigated and compared, such as geometric sematic GP (GSGP), GSGP with local search (HYBRID), and HYBRID with linear scaling (HYBRID-LIN). Their results indicated that the HYBRID-LIN technique provided better results than the other techniques (i.e., GP, HYBRID). Deep learning techniques in AI have also been developed to estimate the energy efficiency of EEB systems (i.e., CL) by [PERSON] et al. [15]. The potential of deep learning was exploited and interpreted for a variety of AI models in predicting CL of EEB systems during 24 h, including multiple linear regression (MLR), elastic net, random forest (RF), gradient boosting trees (GBT), SVR, extreme gradient boosting (XGB), and deep learning (DNN). Their results showed that their XGB model with deep learning technique yielded the highest accuracy with an RMSE of 106.5 and MAE of 71.6. The efforts for optimizing an ANN model using uncertainty means and sensitivity analyses were conducted to predict the energy demand of buildings by [PERSON] et al. [16]. Its performance was proven in a short-term prediction of the energy demand of buildings. As a result, their findings showed the powerful potential of the optimized ANN model in predicting the energy demand of buildings with an R\({}^{2}\) of 0.995, and the average relative error is between 2.0% and 11%. [PERSON] [17] also developed an ensemble machine learning model to predict the CL of EEB systems with high accuracy (e.g., RMSE = 158.77, MAE = 112.07, MAPE = 6.17%, and R\({}^{2}\) = 0.990). By the use of a hybrid model (MSRules-particle swarm optimization (PSO)), [PERSON] et al. [18] predicted the CL of EEB systems with a promising result. A similar study for predicting the HL of EEB systems was also performed by [PERSON] et al. [19], using a novel hybrid approach, i.e., M5 Rules-genetic algorithm (GA). By the use of the meta-heuristic algorithms (i.e., PSO, GA) to optimize the M5 Rules model, [PERSON] et al. [18] and [PERSON] et al.