The comparison of some advanced control methods for energy optimization and comfort management in buildings

Esmaeilzadeh, Ahmad; Zakerzadeh, Mohammad Reza; Koma, Aghil Yousefi

doi:10.1016/j.scs.2018.08.038

Cited by 27 publications

(8 citation statements)

References 46 publications

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“…The intelligent control employed in BEMS with regard to advanced energy and comfort management control can be categorized as (i) Learning-based methods, including fuzzy systems [88], neural networks [89], fuzzy with conventional controls [90], and adaptive fuzzy neural network (ANFIS) systems [91], [92], etc. ; (ii) the model-based predictive control (MPC) technique [93], [94]; and (iii) agent-based control systems [15].…”

Section: B Intelligent Controlmentioning

confidence: 99%

“…The classical PID controller parameters are usually tuned only at a particular operating range with constant controller gains; however, the control parameters including the rise time, settling time and steady-state error of the system undergo drastic change if the operating range is changed [90]. The way forward could be to tune the PID controller online, which would improve the control system parameters under a wide operating range [111].…”

Section: ) Pid-fuzzy Controllermentioning

confidence: 99%

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Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

et al. 2021

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Buildings account for a significant amount of energy consumption leading to the issues of global emissions and climate change. Thus, energy management in a building is increasingly explored due to its significant potential in reducing the overall electricity expenses for the consumers and mitigating carbon emissions. In line with that, the greater control and optimization of energy management integrated with renewable energy resources is required to improve building energy efficiency while satisfying indoor environment comfort. Even though actions are being taken to reduce the energy consumption in buildings with several optimization and controller techniques, yet some issues remain unsolved. Therefore, this work provides a comprehensive review of the conventional and intelligent control methods with emphasis on their classification, features, configuration, benefits, and drawbacks. This review critically investigates the different optimization objectives and constraints with respect to comfort management, energy consumption, and scheduling. Furthermore, the review outlines the different methodological approaches to optimization algorithms used in building energy management. The contributions of controller and optimization in building energy management with the relation of sustainable development goals (SDGs) are explained rigorously. Discussions on the key challenges of the existing methods are presented to identify the gaps for future research. The review delivers some effective future directions that would be beneficial to the researchers and industrialists to design an efficiently optimized controller for building energy management toward targeting SDGs.INDEX TERMS Building energy management, controller, optimization, scheduling, sustainable development goals I. INTRODUCTIONPresently, buildings take the lead in consuming a substantial amount of energy, indicating about 40% of global energy consumption, which is responsible to release one-third of greenhouse gas (GHG) emissions [1], [2]. Another report demonstrates that buildings hold 49% of the total energy worldwide in which 60% of the energy is consumed for heating and cooling purposes [3], [4]. The poor management and ineffective control approach of appliances used in the building may result in a significant loss of energy in a

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Section: B Intelligent Controlmentioning

confidence: 99%

Section: ) Pid-fuzzy Controllermentioning

confidence: 99%

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

et al. 2021

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“…Currently, a body of research work has been reported in the literature in response to challenges around HVAC control optimization. Several research works have been proposed to optimize building HVAC system [22,23], and address On-Off control issues under different constraints. The related research work can be summarized as two approaches: low-complexity optimization, and dynamic optimization.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Air-Conditioner On-Off Control Scheme under Extremely Hot Weather Conditions

et al. 2020

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Being reliant on Air Conditioning (AC) throughout the majority of the year, desert countries with extremely hot weather conditions such as Qatar are facing challenges in lowering weariness cost due to AC On-Off switching while maintaining an adequate level of comfort under a wide-range of ambient temperature variations. To address these challenges, this paper investigates an optimal On-Off control strategy to improve the AC utilization process. To overcome complexities of online optimization, a Elman Neural Networks (NN)-based estimator is proposed to estimate real values of the outdoor temperature, and make off-line optimization available. By looking up the optimum values solved from an off-line optimization scheme, the proposed control solutions can adaptively regulate the indoor temperature regardless of outdoor temperature variations. In addition, a cost function of multiple objectives, which consider both Coefficient of Performance (COP), and AC compressor weariness due to On-Off switching, is designed for the optimization target of minimum cost. Unlike conventional On-Off control methodologies, the proposed On-Off control technique can respond adaptively to match large-range (up to 20 ∘ C) ambient temperature variations while overcoming the drawbacks of long-time online optimization due to heavy computational load. Finally, the Elman NN based outdoor temperature estimator is validated with an acceptable accuracy and various validations for AC control optimization under Qatar’s real outdoor temperature conditions, which include three hot seasons, are conducted and analyzed. The results demonstrate the effectiveness and robustness of the proposed optimal On-Off control solution.

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“…The most common tools adopted and used by /for designers with more design-based interfaces are Grasshopper in Rhinoceros and Dynamo in Autodesk Revit with several practical plugins based on different simulation engines, focusing on different features and components of building such as building facade [8] or materials characteristics [9], glazing and shading [10] have been studied. Several other studies have focused on developing optimization frameworks to optimize the energy performance of buildings [11,12]. However, a comprehensive optimization framework with a back and forth process to finding optimal forms is still missing in the available literature.…”

Section: Introductionmentioning

confidence: 99%

Optimization of building form and its fenestration in response to microclimate conditions of an urban area

2020

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Designing building form in urban areas is a complicated process that demands considering a high number of influencing parameters. On the other hand, there has been an increasing trend to design highly fenestrated building envelopes for office buildings to induce higher levels of natural lighting into the workspace. This paper presents a novel optimization framework to design high-performance building form and fenestration configuration considering the impacts of urban microclimate in typical and extreme weather conditions during a thirty-year period of climate data (2010-2039). In this regard, based on the introduced technique and algorithm, the annual energy demand and thermal comfort of over 8008 eligible form combinations with eight different fenestration configurations and seven different building orientation angels were analysed in a detailed urban area to find optimal design solutions in response to microclimate conditions. Results showed that adopting the framework, annual heating, and cooling demand can be reduced by 21% and 38% while maintaining thermal comfort by taking design-based decisions at the early stages of design.

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The comparison of some advanced control methods for energy optimization and comfort management in buildings

Cited by 27 publications

References 46 publications

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

An Optimal Air-Conditioner On-Off Control Scheme under Extremely Hot Weather Conditions

Optimization of building form and its fenestration in response to microclimate conditions of an urban area

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