###### Abstract  
Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies. energy management; load forecasting; heating and cooling; machine learning; multi-layer perceptron (MLP); support vector regression (SVR) +
Footnote †: journal: Applied  
applications are elevators, heating ventilation and air conditioning (HVAC), domestic hot water, and so on. Among the aforementioned energy sources, optimal operation of HVAC and indoor condition supply are two important factors in evaluating building energy performance [4; 5]. HVAC, as a basic infrastructure in the building, plays an important role by changing the amount of internal cooling and heating loads of residential buildings. Despite the need for this system in buildings, there is a major concern that about 40% of all energy, especially in office buildings, is consumed by this system [6; 7]. Forecasting the thermal loads plays an important role in optimizing the cooling and heating cost of the buildings, as the deviation from the optimally scheduled values will increase the total cost considerably [8]. Energy forecasting is a way to reach optimal operation of HVAC and energy management of residential buildings. In this regard, residential buildings' consumption patterns could be predicted [9]. Nowadays, by developing technology many small-scale smart devices and building management systems (BMSs) can be installed on the residential buildings sites in order to monitor and record the load patterns of the buildings, and also environmental characteristics that have a high impact on the energy forecasting. Using such data, building consumption patterns could be predicted and controlled hourly. In addition, the necessity of energy forecasting has been expressed by some researches e.g., a review study of thermal energy consumption in the buildings pertinent to the comfort was introduced in [10] and the purpose of the study was showing how social-economic, fuel mix and climate change are affected by thermal energy comfort. Moreover, by load forecasting, buildings can schedule for the next day, not only to participate in the demand-response programs [11; 12; 13] but also to participate in energy trading [14; 15]. So far, many studies have been done in order to evaluate load forecasting of the buildings. In a valuable study [16], an integrated design approach has been utilized to estimate life-cycle energy savings, cost-effectiveness of energy efficiency measures in new buildings, and carbon emission reduction. In [17], a multi-objective optimization for energy refurbishments of existing buildings through energy efficiency measures and HVAC systems have been carried out using a genetic algorithm coupled with a dynamic simulation tool.