Low-carbon optimization in commercial buildings through data mining

Abstract

The government of China commits to achieve peak carbon dioxide emissions by 2030. According to the United Nations Environment Programme, nearly 40% of energy-related carbon dioxide emissions are attributable to the building sector while building operations account for over 70% of carbon emissions. How to quantitatively analyze the factors related to operational carbon emissions under the circumstance of rapid growth of data volume is the key problem to be solved. This article explored the main factor affecting carbon emissions of commercial office buildings based on data mining and analyzed all carbon sources using the variation and deviation method in the city of Beijing, China. It is found that electricity, trains, airplanes, and hotels have a great impact on the building’s operation stage carbon emission, and targeted carbon emission reduction policies and measures can be made to reduce the sample’s carbon emission.