Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

Qolomany, Basheer; Al-Fuqaha, Ala; Gupta, Ajay; Benhaddou, Driss; Alwajidi, Safaa; Qadir, Junaid; Fong, A.C.M.

doi:10.1109/access.2019.2926642

Cited by 139 publications

(47 citation statements)

References 192 publications

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“…The review work presented in [25] highlighted the possibility of transforming normal buildings into smart buildings by deploying sensors and Internet of Things (IoT). Working with machine learning and big data analytics, such buildings would provide a lot of automated services to create a comfortable indoor environment for the occupants.…”

Section: B Data Collection Methods For Thermal Comfort Studymentioning

confidence: 99%

“…Subsequently, the prediction outcome can serve as the basis to control the building HVAC systems in order to improve human comfort. HVAC control systems enable the building to provide a specified level of temperature, humidity, ventilation rate and air quality with the aim to achieve optimization of thermal comfort and energy consumption [21]- [25]. In such a manner, the three components -environment, human and HVAC control work together as a closed-loop control system that contributes to a smart building as illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

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Human Comfort in Indoor Environment: A Review on Assessment Criteria, Data Collection and Data Analysis Methods

Song

Mao

Liu³

2019

IEEE Access

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Occupants' comfort perception about the indoor environment is closely linked to their health, wellbeing and productivity. Improvement of comfort level in office buildings has significant positive impacts on both employers and employees. Human comfort in indoor environment usually can be assessed in four aspects: thermal comfort, visual comfort, acoustic comfort and respiratory comfort. In this paper, we present a literature review on the previous research contributions towards studying various aspects of human comfort with a special focus on the respective assessment criteria, data collection methods and data analysis approaches employed by former studies. Previous review work has covered the fundamental concepts associated with human comfort. However, their studies mainly focus on thermal comfort and there is limited work that covers other aspects of comfort. Moreover, few of them discuss how the data is obtained, how to extract useful information from the data and how the data is analyzed. To fill up this gap, this paper conducts the survey from the data-driven point of view. Through the survey, we find that sensor technology has been widely used in the data collection for various types of comfort, while so far the machine learning approaches are mainly applied in the area of thermal comfort study. Finally, some potential future research areas are proposed based on the current status of the research work. The established knowledge in this paper would provide useful insights for engineers or researchers who embark on their research in this area. INDEX TERMS Human comfort, thermal comfort, assessment criteria, data analysis method, sensor technology, machine learning.

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Section: B Data Collection Methods For Thermal Comfort Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human Comfort in Indoor Environment: A Review on Assessment Criteria, Data Collection and Data Analysis Methods

Song

Mao

Liu³

2019

IEEE Access

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“…Moreover, the energy demand of buildings is expected to increase by 50% in the next 30 years [4]. Under the above background, smart buildings have received more and more attention in recent years, which can provide sustainable, economical and comfortable operation environments for occupants using many advanced technologies, e.g., Internet of Things (IoT), cloud computing, machine learning and big data analytics [5]- [7]. To support the above features, Smart Building Energy Management (SBEM) is of great importance [8].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Smart Home Energy Management

Xie

et al. 2020

IEEE Internet Things J.

252

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In this paper, we investigate an energy cost minimization problem for a smart home in the absence of a building thermal dynamics model with the consideration of a comfortable temperature range. Due to the existence of model uncertainty, parameter uncertainty (e.g., renewable generation output, nonshiftable power demand, outdoor temperature, and electricity price) and temporally-coupled operational constraints, it is very challenging to determine the optimal energy management strategy for scheduling Heating, Ventilation, and Air Conditioning (HVAC) systems and energy storage systems in the smart home. To address the challenge, we first formulate the above problem as a Markov decision process, and then propose an energy management strategy based on Deep Deterministic Policy Gradients (DDPG). It is worth mentioning that the proposed strategy does not require the prior knowledge of uncertain parameters and building thermal dynamics model. Simulation results based on real-world traces demonstrate the effectiveness and robustness of the proposed strategy.

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“…Runge & Zmeureanu (2019) [2] and Bourdeau et al (2019) [3] reviewed studies on using machine learning for building energy consumption forecasting. Qolomany et al (2019) [4] and Djenouri et al (2019) [5] reviewed how machine learning and big data could be applied to smart buildings. Vázquez-Canteli & Nagy (2019) [6] and Mason, K. & Grijalva (2019) [7] reviewed how reinforcement learning, a subdomain of machine learning, could be used to enhance building control.…”

Section: Introductionmentioning

confidence: 99%

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

Hong

Wang

Luo

et al. 2020

Energy and Buildings

201

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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.

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Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

Cited by 139 publications

References 192 publications

Human Comfort in Indoor Environment: A Review on Assessment Criteria, Data Collection and Data Analysis Methods

Human Comfort in Indoor Environment: A Review on Assessment Criteria, Data Collection and Data Analysis Methods

Deep Reinforcement Learning for Smart Home Energy Management

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

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