Smart Estimation Model for Energy Performance Certificates of Residential Buildings
The wasted amount of energy has become a critical problem for many countries because of limited energy sources and the cost of energy production. These forced countries to increase energy usage awareness by making regulations in the construction sector, which might dramatically decrease energy consumption because of the size of the domain. One of those regulations is standardizing heating and cooling loads (HL/CL) to avoid waste of energy. HL and CL need an advanced engineering process because of different parameters such as thermal characteristics of the building, hot water supply, passive solar systems, etc. Hence, it can only be carried out by expert engineers in calculations. In this paper, a classification model as a decision support system is proposed for predicting the energy consumption of residents which is an efficient indicator of the architectural features of the construction about the energy consumption concept. The data is collected from architectural projects and energy performance certificates of 127 buildings. Multilayer Perceptrons, Bagging, and Random subspace methods are used to predict the energy class of the buildings. Based on the findings, the most accurate results were achieved by Bagging. Moreover, the main input features affecting the prediction performance of HL were revealed and the classification success was observed.