###### Abstract
Predictor methods for heating load based on artificial neural networks (ANN) have been evaluated in [18] for office buildings where the impact of data size and dimensionality in ANN was inspected. In order for heating, ventilation, and air-conditioning (HVAC) system optimization in [19], electricity load forecasting based on ANN has been studied. Among three utilized algorithms such as Levenberg-Marquardt, Scaled Conjugate gradient back-propagation, and Bayesian Regularization (BR), the BR-based ANN showed the best performance. Another study proposed the energy forecasting method using statistical analysis for heating and cooling of an office building [20]. In [21], four hybrid techniques based on artificial neural network (ANN) and meta-heuristic algorithms such as artificial bee colony (ABC) optimization, particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and genetic algorithm (GA) have been suggested for forecasting the heating load of buildings' energy efficiency. Forecasting the cooling load has been done in [22] using a probabilistic entropy-based neural (PENN) method. Short-term cooling load prediction in order to optimize the operation of HVAC systems and energy efficiency measures in buildings has been done in [23] using multiple nonlinear regression (MNR), auto regressive (AR), and autoregressive with exogenous (ARX) models. In [24], the thermal comfort reduction of the energy consumption in the building by 36.5% was performed via a feedforward neural network (FFNN). A decision tree method has been suggested in [25] for energy demand forecasting and energy efficiency measures of a residential building. A comparative study of forecasting methods for heating and cooling load was done in [26], where machine learning techniques such as a deep neural network (DNN), gradient boosted machine (GBM), Gaussian process regression (GPR) and minimax probability machine regression (MPMR) were compared with each other. In [27], prediction of the cooling and heating loads of the building were done using ANN, classification and regression tree (CART), general linear regression (GLR), and chi-squared automatic interaction detector (CHAID). In the same work, the technical characteristics of the building were considered as input to the networks. In [28], sixteen residential buildings were evaluated in terms of heating and cooling energy consumption forecasting via adaptive linear time-series models. Likewise, cooling load forecasting based on data mining techniques was proposed in [29] to help design a more efficient building management system (BMS). In [30], the BMS based on electrical, economic, and ecological optimization using a genetic algorithm was introduced to improve energy efficiency of the buildings. General regression neural network (GRNN) has also been employed in [31] for cooling energy forecasting to optimize HVAC heat storage of public buildings. In most of the aforementioned research works, meteorological data was used as an indicator and input for forecasting the cooling and heating loads of residential buildings. It is undeniable that environmental and meteorological factors do not affect the cooling and heating loads of residential buildings, but sometimes abrupt changes in weather could disrupt energy forecasting equations, reducing the accuracy coefficient and increasing the error in the energy forecasting operation. In this paper, high-precision prediction of cooling and heating loads of a building was done by using multilayer perceptron (MLP) and support vector regression (SVR) methods. A set of data on structural characteristics of the building was considered as an input variable, while the amount of cooling and heating load was considered as an output variable. Using this data and creating a linear mapping between input and output variables via the proposed methods, it is possible to make a more accurate prediction of cooling and heating loads. The rest of the paper is organized as follows: Section 2 describes the case study and dataset.