A Machine Learning-based Building Operational Pattern Identification

doi:10.23940/ijpe.20.11.p15.18351844

Abstract

Abstract: Creativity has become more important in modern life due to the development of quality of life. The paper first discusses the relationships among creativity, building environment, and machine learning to illustrate that an energy conservative building environment can raise occupants' creativity and satisfaction. Then, an energy conservative way aided by machine learning is proposed. The building operational patterns are identified using this machine-learning model. The studied database builds air-conditioning energy consumption of a Singaporean school. The clustering process first removes inconsistent and null data. Then, four features are selected from the original 18 features based on the domain knowledge and the characteristics of the dataset. The k-means algorithm is used to discover the hidden building operational patterns in the building energy consumption information. The results show that the three derived clusters can be well interpreted by domain knowledge. An energy conservation measure is carried out based on further investigation on the clusters. The data mining process proposed in this article is a creative way for intelligent building. Future research could also be conducted based on the creative method in this article; possible future directions are highlighted in the last section.

Key words: machine learning, building operational patterns, building energy conservation, k-means

Mingzhu Li and Yufeng Deng. A Machine Learning-based Building Operational Pattern Identification [J]. Int J Performability Eng, 2020, 16(11): 1835-1844.

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