A Comparative Study of Machine Learning Algorithms for Short-Term Building Cooling Load Predictions
Abstract: Buildings account for a large part of the total energy demand in the world. The building energy demand increases every year and space cooling is the main contributor for this increase. In order to create a sustainable global energy system it is therefore of great importance to improve the energy efficiency in buildings. Cooling load predictions are an essential part of improving the building energy efficiency. The widespread use of Building Automation Systems (BAS) in modern buildings makes it possible to use data-driven methods for such predictions. The purpose of this study is twofold: to compare the performance of five different machine learning algorithms by analyzing their accuracy and robustness; and to examine what effect different versions of a data set have on these algorithms. The data that is used in this study is one-year operational data from a building in the city of Shenzhen in China. This data set is engineered in multiple different ways and is used to test the algorithms. This study show that the predictive accuracy of machine learning models can be improved by introducing time-lag variables into the data set, especially if the original data set is very low-dimensional. It is also shown that some algorithms benefit from a binary representation of calendar variables instead a decimal representation. All algorithms in this study show quite similar results which suggests that they are all capable of capturing the relationships in the data. The accuracy of all models that are developed in this study are considered good enough for engineering purposes.
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