# Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques
## 1 Introduction  
In recent decades, artificial intelligence-based methods have been dramatically applied by scientists in different fields of study, particularly in energy systems engineering (such as in [PERSON] et al. [1] and [PERSON] et al. [2]). In this regard, scientific applications of machine learning-based techniques were considered to be a proper alternative in order to forecast the quantity of energy in constructions. Consequently, an appropriate inspection of the particular energy performance for buildings and optimalcontribying of the heating, ventilation, and air-conditioning (HVAC) system will help in pushing further sustainable consumption related to energy. The world's energy consumption is still maintained at a high value and even though many countries have taken some reasonable measures it is expected that energy consumption will increase in future. Many believe that this is because of the rapid expansion of economy and the improvement of living requirements. Currently, energy required for buildings accounts for almost 40% of all energy use in Europe [3]. Some reports have indicated that in countries such as United States and China, this value accounts for about 39% of the whole energy demand along with 27.5% of nationally consumed energy. As a novel idea, most recently, intelligent predictive tools have been utilized for the field of energy consumption calculation. In fact, the problem of heating load calculation in energy-efficient buildings is an established concern. For realizing the best artificial intelligence (AI) model to meet this goal, this study provides and compares five well-known models that are widely used by researchers [4; 5; 6; 7; 8]. Similar to other research in the fields of science and technology, AI techniques have widespread application in order to put forward reasonable evaluation in many engineering problems [9; 10; 11; 12; 13; 14; 15; 16; 17] of the energy consumption in buildings. In numerous types of artificial intelligence-based solutions, artificial neural network (ANN) is known as a recognized method that is largely employed for many prediction-based examples [18; 19; 20; 21; 22]. Similar studies are performed in regard to hybrid metaheuristic optimization approaches [23; 24; 25; 26; 27; 28; 29]. Also, in the field of energy management, neural networks have emerged as one of the effective prediction tools [30; 31; 32; 33]. [PERSON] et al. [34] investigated the design optimization of energy efficient buildings by employing several evolutionary neural networks. The methods were applied to drive the design of a typical facade module (i.e., play a key role in the definition of the energy performance of buildings) for an office building. [PERSON] and [PERSON] [35] employed various data mining-based solutions in order to predict the energy performance of buildings and to facilitate early designs of energy conserving buildings. These techniques include support vector regression (SVR), ANN, regression and classification tree, ensemble inference model, general linear regression, and chi-squared automatic interaction detector. [PERSON] et al. [22] studied the challenges and advances in data mining applications for communities concerning energy-efficient buildings. [PERSON] et al. [36] employed a neural network model in an energy-efficient building to achieve proper smart lighting control. [PERSON] [20] reviewed different techniques of optimization applied to the energy-efficient building. [PERSON] and [PERSON] [37] investigated smart buildings and their efficient energy consumption. In this regard, various prediction-learning algorithms including particle-based hybrid optimization algorithm were employed and their performances were evaluated. [PERSON] [18] explored the excellent capacity of machine learning to assess early predicting cooling loads. The main objective of such prediction was prediction of cooling loads in the office buildings through machine learning-based solutions. His study successfully achieved the objective by providing some neural network-based equations.