An agent-based distributed real-time optimal control strategy for building HVAC systems for applications in the context of future IoT-based smart sensor networks

Su, Bing; Wang, Shengwei

doi:10.1016/j.apenergy.2020.115322

Cited by 33 publications

(15 citation statements)

References 26 publications

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“…The rapid evolution of such networks with the evolution in technology and uses has also been discussed in the literature [2,8]. The role of middleware or an agent-based architecture has also been advocated by other researchers and experts [11][12][13]. Therefore, in this paper, we report the proposed candidate architectures having distributed agent-based architecture and their evaluation.…”

Section: Related Workmentioning

confidence: 94%

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Smart and Adaptive Architecture for a Dedicated Internet of Things Network Comprised of Diverse Entities: A Proposal and Evaluation

Singh¹,

Ali²,

Lundberg³

2022

Sensors

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Advances in 5G and the Internet of Things (IoT) have to cater to the diverse and varying needs of different stakeholders, devices, sensors, applications, networks, and access technologies that come together for a dedicated IoT network for a synergistic purpose. Therefore, there is a need for a solution that can assimilate the various requirements and policies to dynamically and intelligently orchestrate them in the dedicated IoT network. Thus we identify and describe a representative industry-relevant use case for such a smart and adaptive environment through interviews with experts from a leading telecommunication vendor. We further propose and evaluate candidate architectures to achieve dynamic and intelligent orchestration in such a smart environment using a systematic approach for architecture design and by engaging six senior domain and IoT experts. The candidate architecture with an adaptive and intelligent element (“Smart AAA agent”) was found superior for modifiability, scalability, and performance in the assessments. This architecture also explores the enhanced role of authentication, authorization, and accounting (AAA) and makes the base for complete orchestration. The results indicate that the proposed architecture can meet the requirements for a dedicated IoT network, which may be used in further research or as a reference for industry solutions.

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Section: Related Workmentioning

confidence: 94%

“…Existing research artifacts have analyzed and presented the importance of middleware or an agent for control, management, and orchestration in such a diverse and evolving IoT network [11][12][13]. These agents can be federated and distributed.…”

Section: Introductionmentioning

confidence: 99%

Smart and Adaptive Architecture for a Dedicated Internet of Things Network Comprised of Diverse Entities: A Proposal and Evaluation

Singh¹,

Ali²,

Lundberg³

2022

Sensors

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“… 2014 ). This is possible by networking a plethora of sensors and components responsible for the monitoring and operation of mechanical, security, fire, lighting, HVAC and humidity control and ventilation systems (Su and Wang 2020 ).…”

Section: Introductionmentioning

confidence: 99%

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

et al. 2022

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In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings.

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“…With the combination of BAS and distributed technologies, many distributed algorithms are developed for device optimizations 36 , 37 and indoor climate control 38 , 39 to reduce energy consumption and improve thermal comfort. The smart-building concept comes from the fusion of BAS and IoT with building perceptions, networks, and clouds.…”

Section: Introductionmentioning

confidence: 99%

“…propose a semi-distributed framework for HVAC and variable air volume (VAV) systems with ADMM-based algorithms. 36 , 37 Simulation results show that their suggested algorithms provide superior energy-saving effects compared with existing control methods. Here, we use “semi” to distinguish this approach from a fully distributed framework since a central coordinating agent is required for the global optimization.…”

Section: Introductionmentioning

confidence: 99%

Honeycomb: An open-source distributed system for smart buildings

Xing

Sun

et al. 2022

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An agent-based distributed real-time optimal control strategy for building HVAC systems for applications in the context of future IoT-based smart sensor networks

Cited by 33 publications

References 26 publications

Smart and Adaptive Architecture for a Dedicated Internet of Things Network Comprised of Diverse Entities: A Proposal and Evaluation

Smart and Adaptive Architecture for a Dedicated Internet of Things Network Comprised of Diverse Entities: A Proposal and Evaluation

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

Honeycomb: An open-source distributed system for smart buildings

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