Conceptual architecture of building energy management open source software (BEMOSS)

Khamphanchai, Warodom; Saha, Avijit; Rathinavel, Kruthika; Kuzlu, Murat; Pipattanasomporn, Manisa; Rahman, Saifur; Akyol, Bora A.; Haack, Jereme

doi:10.1109/isgteurope.2014.7028784

Cited by 45 publications

(22 citation statements)

References 3 publications

(2 reference statements)

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“…Building Energy Management Open Source Software (BEMOSS) [22] is an open source operating system that is expected to improve sensing and control of equipment to reduce energy consumption. This focusses on user management in addition to the operating system and connectivity [31].…”

Section: Reference Architecturementioning

confidence: 99%

“…The data acquisition, processing, and analysis operations are performed on the information received from IED. The connectivity layer [22] takes care of the communication between the operating system and the framework layer and all physical hardware devices. This encompasses different communication technologies, data exchange protocols, and device functionalities.…”

Section: Layered Architecture In Smart Grid Softwarementioning

confidence: 99%

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Software Architectures for Smart Grid System—A Bibliographical Survey

et al. 2019

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Smart grid software interconnects multiple Engineering disciplines (power systems, communication, software and hardware technology, instrumentation, big data, etc.). The software architecture is an evolving concept in smart grid systems, in which system architecture development is a challenging process. The architecture has to realize the complex legacy power grid systems and cope with current Information and Communication Technologies (ICT). The distributed generation in a smart grid environment expects the software architecture to be distributed and to enable local control. Smart grid architecture should also be modular, flexible, and adaptable to technology upgrades. In this paper, the authors have made a comprehensive review of architectures for smart grids. An in depth analysis of layered and agent-based architectures based on the National Institute of Standards and Technology (NIST) conceptual model is presented. Also presented is a set of smart grid Reference Architectures dealing with cross domain technology.

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Section: Reference Architecturementioning

confidence: 99%

Section: Layered Architecture In Smart Grid Softwarementioning

confidence: 99%

Software Architectures for Smart Grid System—A Bibliographical Survey

et al. 2019

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“…Besides, it is employed as the base of other platforms. The BEMOSS TM system [43,44] developed by Virginia Tech serves as a good example. It adds auto-discovery capability for several devices together with a user interface for controlling those devices.…”

Section: Extended Distributing Stepmentioning

confidence: 99%

Multi-agent systems and their applications

Xie

Liu

2017

Journal of International Council on Electrical Engineering

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The number of distributed energy components and devices continues to increase globally. As a result, distributed control schemes are desirable for managing and utilizing these devices, together with the large amount of data. In recent years, agent-based technology becomes a powerful tool for engineering applications. As a computational paradigm, multi-agent systems (MASs) provide a good solution for distributed control. In this paper, MASs and applications are discussed. A state-ofthe-art literature survey is conducted on the system architecture, consensus algorithm, and multiagent platform, framework, and simulator. In addition, a distributed under-frequency load shedding scheme is proposed using the MAS. Simulation results for a case study are presented. The future of MASs is discussed in the conclusion.

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“…Several works propose architectural approaches for the smart integration of appliances, devices, and energy management facilities. For example, Khamphanchai et al [19] proposed a platform for energy management of buildings that is based on distributed agents. Furthermore, architectures based on the IoT paradigm are presented in [20,21].…”

Section: Introductionmentioning

confidence: 99%

Towards the Real-World Deployment of a Smart Home EMS: A DP Implementation on the Raspberry Pi

et al. 2019

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As the adoption of distributed generation and energy storage grows and the attention to energy efficiency rises, Energy Management is assuming a growing importance in smart homes. Energy Management Systems (EMSs) should be easily deployable on smart homes and seamlessly integrate with the Internet of Things (IoT) ecosystem, including generators and storage devices. This paper redesigns a previously presented EMS to reduce its computational complexity, implement it on a Raspberry Pi, and make it compatible with the IoT paradigm. The EMS manages the power flows between smart home loads, renewable generators, electrical storage, and power grid. It communicates with a network of wireless sensors for electrical appliances and with a cloud-based utility data aggregator. The EMS uses Artificial Intelligence and a Dynamic Programming algorithm to fulfill two objectives at the same time: lowering the end user’s electricity bill and reducing the uncertainty on the power exchanged between the end user and the grid manager. The latter goal is obtained by an effective compensation of forecasting errors. A test bench emulating four smart homes was used to measure the effectiveness of the EMS and the efficiency of the proposed implementation. The results show an uncertainty of the aggregated exchanged power of only 2.88% and a reduction of the electrical bill for end-users of up to 3.23%. Furthermore, the EMS can complete its most onerous task in less than 9 min. The good performance of the proposed EMS makes it a candidate for fast adoption by the market.

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Conceptual architecture of building energy management open source software (BEMOSS)

Cited by 45 publications

References 3 publications

Software Architectures for Smart Grid System—A Bibliographical Survey

Software Architectures for Smart Grid System—A Bibliographical Survey

Multi-agent systems and their applications

Towards the Real-World Deployment of a Smart Home EMS: A DP Implementation on the Raspberry Pi

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