Analyzing the Effects of the Dwelling Characteristics on the Space Heating Costs with Bayesian Networks

Energy is one of the main concerns of humanity because energy resources are limited and costly. Hence, effective use of energy is important. In order to reduce the costs and to use the energy for space heating effectively, new building materials, techniques and insulations facilities are being developed. Therefore, it is important to know which factors affect the space heating costs. This study aims to introduce the use of Bayesian networks to analyze the effects of dwelling characteristics on the space heating costs. The Bayesian Network model shows that the space heating costs of the dwellings are mostly affected by the heating systems used. The second important factor appears to be the existence of external wall insulation. The third most important factor, however, is the building age. Additionally, dwellings on the ground floors and on the first floors appear to pay the highest space heating costs. On the other hand, dwelling type and facing direction seem not to have a considerable effect on the space heating costs.