## 1 Introduction  
Smart cities are the development goals of many countries around the world [1]. Intelligent systems have been widely researched and applied in smart cities to provide a better quality of life, as well as to bring higher economic efficiency [2; 3; 4; 5; 6]. One of the critical issues of smart cities is the efficient use of energy by buildings [7; 8; 9]. Of those, energy for cooling or heating the buildings is significant since they take a considerable part of the total energy [10]. In winter, the demand for energy for the heating load (HL) is significant [11]. The ineffective use of HL not only results in economic losses but also a threat to the surrounding environment [12]. Therefore, accurately predicting the HL of the buildings is a challenge for smart cities. For estimating HL of building systems, many efforts of scientists have been implemented to research and propose artificial intelligence (AI) systems, as advanced alternative techniques to control the HL of buildings. [PERSON] et al. [13] attempted to develop an artificial neural network (ANN) to predict the heating system in building with a promising result. An ANN model with the back-propagation algorithm was optimized based on the learning rate, moment, hidden layer number, nodes of the hidden layer, bias, and learning factors, for their aim. Finally, the heating system was accurately predicted with a determination coefficient (R\({}^{2}\)) of 0.988 in their study. In another study, [PERSON] et al. [14] used a regression analysis to evaluate energy consumption in the UK. Unlike the previous study, their study investigated the energy consumption of a supermarket based on electricity and gas used. The humidity ratio was analyzed and used based on the relative humidity and the dry-bulb temperature for their aim. A long-term prediction of energy consumption for the supermarket was implemented from 2030 to 2059. Their findings showed that whereas gas consumption reduces 13%, electricity use will increase by 2.1%. Based on ANN techniques and their ensemble, [PERSON] et al. [15] successfully developed a model to predict heating energy consumption, using feedforward backpropagation neural network (FFNN), adaptive neuro-fuzzy interference system (ANFIS), and radial basis function network (RBFN); 35 buildings with the total area of 300,000 m\({}^{2}\) were considered in their study. Different input variables were used for each type of neural networks. Subsequently, three neural network models were developed (i.e., FFNN, RBFN, and ANFIS). Eventually, the outcome predictions of the three neural network models were integrated to generate the new ensemble model with high accuracy (i.e., root-mean-squared error--RMSE = 8169.1, MAPE = 5.3204, and R\({}^{2}\) = 0.9845). A dynamic ANN model with the use of the Taguchi method was also developed by [PERSON], [PERSON] [16], for predicting the HL of a building with high accuracy. Dew point temperature, diffuse horizontal radiation, dry-bulb temperature, wind speed, and direct normal radiation, were used as the input variables in their study for the development of an ANN model to predict HL. Then, the Taguchi method was applied to define the effect of the input variables on HL. As a result, the wind speed and outdoor temperature were determined as the most influential parameters on HL in their study, and an ANN model was developed based on the selected parameters. Finally, a promising result with an RMSE of 37.3 and R\({}^{2}\) of 0.999. [PERSON] et al. [17] also developed an inverse black-box technique to model HL and the cooling load of five office buildings. Electrical load data, hourly cooling, heating, as well as the solar irradiance, wind speed, temperature, and humidity, were collected for their aim. They concluded that their black-box technique could model the HL and cooling load of the buildings very well, and it was introduced as an alternative operational decision-making process.