Conventional machine learning (ML) techniques based on big data are difficult to integrate directly into building operations due to the curse of dimensionality caused by data sparsity. While the number of feature variables in buildings is considerable, in many cases, a reliable dataset is only obtained from the target building operation through unique features. This results in insufficient data for building an operable ML model. In this study, we proposed a methodology applying a robust algorithm and carefully selected feature variables based on building physics. An operation using climate-adaptive building shells is presented as a case study. Energy simulations utilizing a generic office building model equipped with electrochromic glasses were performed on 2016-2019 weather data from Tokyo and Fukuoka, Japan. The k-nearest neighbor algorithm was employed for the ML application because of its robustness regarding small datasets, and feature variables were prearranged and carefully chosen to set an adequate combination between the numbers of feature and objective variables. Without ML, the air conditioning system operation became unstable in intermediate seasons. The ML application successfully solved this problem; 95% of the undesired cooling operation was avoided. The results prove that a simple ML algorithm could become a better solution than a complex one in cases where building physics based on building engineers' knowledge is effectively utilized. It expands the ML application in various building operations, even in cases that do not respond to the direct application of complex ML techniques that require large datasets, known as big data.
Hiyama, K; Omodaka, Y
Operation of climate-adaptive building shells utilizing machine learning under sparse data conditions
Journal Of Building Engineering