Intelligent multi-agent system for building heat distribution control with combined gas boilers and ground source heat pump

Mokhtar, Maizura; Stables, M. A.; Liu, Xiongwei; Howe, Joe

doi:10.1016/j.enbuild.2013.03.045

Cited by 32 publications

(13 citation statements)

References 19 publications

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“…Simulations results showed that 3% increase in overall comfort level and approximately 9% of the total energy was saved. Mokhtar et al (2013) proposed an ARTMAP multiagent BMS system. ARTMAP is a type of ANN that provides incremental learning inspired by how human processes memory and learns new information without necessarily forgetting the previously learned information (Carpenter et al 1991).…”

Section: Mas Applicationsmentioning

confidence: 99%

Computational intelligence techniques for HVAC systems: A review

et al. 2016

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Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO 2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air-conditioning (HVAC) systems are the major source of energy consumption in buildings and ideal candidates for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems. The analysis of trends reveals that the minimisation of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences.Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE-2, HVACSim+ and ESP-r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on multi-agent systems (MAS), as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions.

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Section: Mas Applicationsmentioning

confidence: 99%

Computational intelligence techniques for HVAC systems: A review

et al. 2016

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“…Smart and energyefficient building are also the focus of some MAS applications [117], such as building heat distribution control [118]. A multiagent home automation system for power management was idealized by Abras et al [119] and is an interesting guideline for this kind of MAS application.…”

Section: Microgrid Demand Controlmentioning

confidence: 99%

Multi-agent systems applied for energy systems integration: State-of-the-art applications and trends in microgrids

et al. 2017

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“…Summarizing, while currently proposed systems demonstrate promising results in energy reduction and comfort preservation, most of them have been assessed only through simulations 2,11,[13][14][15]17,18,[20][21][22][23][24][26][27][28][31][32][33] that do not fully embody the complexities of a real-life commercial or tertiary building, even if real life building data were used as input for the simulations (eg, Agarwal et al 31 ). Moreover, some of the real-case applications are very limited in terms of deployment (eg, Goyal et al 5 ) and cannot be easily assessed in full.…”

Section: Novelty Statementmentioning

confidence: 99%

Occupancy-based decision support system for building management: From automation to end-user persuasion

et al. 2019

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Summary In this paper, an intelligent occupancy‐based, automated building control system is initially introduced, which has the capability of reducing energy consumption while respecting the occupants' comfort and actions inside building areas. Data stemming from a multisensorial network is combined with implicitly extracted information, since no direct feedback is expected by the users, towards unsupervised monitoring and control of building operation. However, due to significantly higher costs of actuating equipment and the reluctance that some end users show in accepting fully automated solutions, a new hybrid approach is conceptualized and presented; the automated decision support system is supplemented with a persuasion mechanism aimed at increasing energy savings, through raising user awareness. The persuasive methodology employs a continuous feedback mechanism in order to select the optimal incentive strategy by taking into account user success rates, in terms of requested actions, as well as available mechanisms, and daily harassment.

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Intelligent multi-agent system for building heat distribution control with combined gas boilers and ground source heat pump

Cited by 32 publications

References 19 publications

Computational intelligence techniques for HVAC systems: A review

Computational intelligence techniques for HVAC systems: A review

Multi-agent systems applied for energy systems integration: State-of-the-art applications and trends in microgrids

Occupancy-based decision support system for building management: From automation to end-user persuasion

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