The Application of Statistical and Machine Learning Techniques in Building Performance Assessment and Prediction: A Review

Achieving energy efficiency, zero emissions, resiliency, and healthfulness are important subjects facing the current field of buildings. Building performance simulation is the means established and accredited to quantify building performance and thus enables the communication of building energy efficiency and carbon emissions information. However, traditional building performance physics-based simulation presents significant challenges and shortcomings, such as being complex, time-consuming, and divergent when compared to actual performance. There remains a need for ease of use, immediate simulation, and accurate building performance prediction approaches. Emerging statistical and machine learning techniques open the possibility of developing a novel prediction model with ease of use, immediate simulation, and accurate features. Investigating the application of statistical and machine learning techniques in building performance prediction has been an attractive research direction. However, the published knowledge on applying statistical and machine learning techniques in building performance prediction remains inadequate and insufficient in terms of scalability and universality. This paper provides an up-To-date review of the application of statistical and machine learning techniques in building performance prediction, intending to recommend the latest research status and enlighten future research points. A comprehensive discussion on the impetus and strengths of applying statistics and machine learning in building performance is highlighted. In contrast, the limitations of existing applications and recommendations for future research are pronounced. Distinct from similar reviews that cover a broader application range, this paper focuses on the prediction application of whole building performance.