Modeling Heating and Cooling Loads in Buildings Using Gaussian Processes

The basic principle of the building energy efficiency is to use less energy for operations such as heating, cooling, lighting and other appliances, without impacting the health and comfort of its occupants. In order to measure energy efficiency in a building, it is necessary to estimate its heating and cooling loads, considering some of its physical characteristics such as geometry, material properties as well as local weather conditions, project costs and environmental impact. Machine Learning Methods can be applied to solve this problem by estimating a response from a set of inputs. This paper evaluates the performance of Gaussian Processes, also known as kriging, for predicting cooling and heating loads of residential buildings. The dataset consists of 768 samples with eight input variables and two output variables derived from building designs. The parameters were selected based on exhaustive search with cross validation. Four statistical measures and one synthesis index were used for the performance assessment and comparison. The results show Gaussian Processes consistently outperform other machine learning techniques such as Neural Networks, Support Vector Machines and Random Forests. The proposed framework resulted in accurate prediction models contributing to savings in the initial phase of the project avoidlng the modeling and testing of several designs.