State-of-the-art on research and applications of machine learning in the building life cycle

Abstract

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.

Online Implementation of a Soft Actor-Critic Agent to Enhance Indoor Temperature Control and Energy Efficiency in Buildings

“…As a consequence, research has also focused on the development of model-free controllers based on Machine Learning (ML), which has shown great potential for improving the performance of buildings [19], particularly when Reinforcement Learning (RL)based algorithms [20] are implemented. The interest in control strategies based on RL has increased since it does not require prior knowledge of the process or environment to be controlled.…”

Review on Fault Detection and Diagnosis Feature Engineering in Building Heating, Ventilation, Air Conditioning and Refrigeration Systems

“…Recently, it has become clear that -compared with the quantitative and qualitative model-based methods, -the data-based FDD methods are superior especially for largescale HVAC&Rs [29]. For applications in HVAC&Rs, FDD studies have implemented several data analytics algorithms, including machine learning [30][31][32][33], data mining [34][35][36][37], statistics [38][39][40], decision-making [41][42][43][44][45][46], expert system [40,47,48] and pattern recognition [49,50] algorithms. From the perspective of data analytics, the objective of fault detection is to ascertain whether a given data sample lies Feature engineering (FE) is an important step in generating the optimal model inputs for the FDD strategy in HVAC&Rs.…”

Random forest regression analysis on combined role of meteorological indicators in disease dissemination in an Indian city: A case study of New Delhi

“…Statistical analysis is one of the popular methods to assess the correlation between the climate-epidemic factors. In the recent years, machine learning models have become cost-effective alternatives to accurately analyze a wide variety of data ( Malki et al, 2020 ; Smiti, 2020 ; Roohi et al, 2020 ; Hong et al, 2020 ).…”