Advanced Fuzzy-Logic-Based Context-Driven Control for HVAC Management Systems in Buildings

Morales, Luis; Aguilar, José; Garcés-Jiménez, Alberto; Mesa, José Antonio Gutiérrez de; Pulido, José Manuel Gómez

doi:10.1109/access.2020.2966545

Cited by 48 publications

(33 citation statements)

References 56 publications

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“…Continuous regulation is widely performed with classic and inexpensive proportional integral derivative (PID) controllers. Nonlinearities, like partial loads, requiring self-tuning techniques, such as relay-autotuning, open-loop step tests and more recently fuzzy logic, Ziegler-Nichols or Cohen-Coon methods [4]. HVAC also requires multiple input and multiple output variables (MIMO) handling, like splitting the mechanism into several SISO subsystems-or just the PID.…”

Section: Hvac Systemsmentioning

confidence: 99%

“…HVAC also requires multiple input and multiple output variables (MIMO) handling, like splitting the mechanism into several SISO subsystems-or just the PID. This in any case remains difficult to stabilize [4]. Complexity arises in applying multi-objective optimization requiring advanced control methods.…”

Section: Hvac Systemsmentioning

confidence: 99%

“…Some of them are normally given by the manufacturer in the technical specifications under standard working conditions and specifically, for the two heat pumps and the two water-water coolers in the Teatro Real of Madrid, Table 5 shows the information. Particularly, in a previous work has been introduced the autonomous management architecture of the multi-HVAC model based on ACODAT that sear to optimize the configuration of the HVAC subsystems in a given moment improve the energy efficiency and costs, and other work that proposes a fuzzy-based control method for HVAC [4], which can be incorporated in the management system. This study is a continuation of the previous research and introduces the concept of ACODAT for the supervision of building multi-HVAC systems.…”

Section: Experiments Contextmentioning

confidence: 99%

See 2 more Smart Citations

An Autonomic Cycle of Data Analysis Tasks for the Supervision of HVAC Systems of Smart Building

Aguilar¹,

Ardila²,

Avendaño³

et al. 2020

Energies

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Early fault detection and diagnosis in heating, ventilation and air conditioning (HVAC) systems may reduce the damage of equipment, improving the reliability and safety of smart buildings, generating social and economic benefits. Data models for fault detection and diagnosis are increasingly used for extracting knowledge in the supervisory tasks. This article proposes an autonomic cycle of data analysis tasks (ACODAT) for the supervision of the building’s HVAC systems. Data analysis tasks incorporate data mining models for extracting knowledge from the system monitoring, analyzing abnormal situations and automatically identifying and taking corrective actions. This article shows a case study of a real building’s HVAC system, for the supervision with our ACODAT, where the HVAC subsystems have been installed over the years, providing a good example of a heterogeneous facility. The proposed supervisory functionality of the HVAC system is capable of detecting deviations, such as faults or gradual increment of energy consumption in similar working conditions. The case study shows this capability of the supervisory autonomic cycle, usually a key objective for smart buildings.

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Section: Hvac Systemsmentioning

confidence: 99%

Section: Hvac Systemsmentioning

confidence: 99%

Section: Experiments Contextmentioning

confidence: 99%

See 1 more Smart Citation

An Autonomic Cycle of Data Analysis Tasks for the Supervision of HVAC Systems of Smart Building

Aguilar¹,

Ardila²,

Avendaño³

et al. 2020

Energies

Self Cite

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“…Some early work that used fuzzy-logic based approach for HVAC control includes [6][7][8][9]. Recently, Escobar et al [10] developed a system called LAMDA (learning algorithm for multivariable data analysis) which used a fuzzy-logic based classification approach for HVAC management in building. In another work, Zang et al integrated a genetic algorithm (GA), an artificial neural network (ANN), multivariate regression analysis (MRA), and a fuzzy logic controller (FLC) to optimize the indoor environment and energy consumption based on simulation results [11].…”

Section: Intelligent Hvac Controlmentioning

confidence: 99%

Evaluating the Adaptability of Reinforcement Learning Based HVAC Control for Residential Houses

et al. 2020

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Intelligent Heating, Ventilation, and Air Conditioning (HVAC) control using deep reinforcement learning (DRL) has recently gained a lot of attention due to its ability to optimally control the complex behavior of the HVAC system. However, more exploration is needed on understanding the adaptability challenges that the DRL agent could face during the deployment phase. Using online learning for such applications is not realistic due to the long learning period and likely poor comfort control during the learning process. Alternatively, DRL can be pre-trained using a building model prior to deployment. However, developing an accurate building model for every house and deploying a pre-trained DRL model for HVAC control would not be cost-effective. In this study, we focus on evaluating the ability of DRL-based HVAC control to provide cost savings when pre-trained on one building model and deployed on different house models with varying user comforts. We observed around 30% of cost reduction by pre-trained model over baseline when validated in a simulation environment and achieved up to 21% cost reduction when deployed in the real house. This finding provides experimental evidence that the pre-trained DRL has the potential to adapt to different house environments and comfort settings.

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“…In addition, it is affected by various factors [15]. Consequently, to effectively improve energy efficiency and reduce energy consumption in buildings, we have conducted several investigations [16]- [18] with the aim of optimizing the performance of HVAC systems in historic or unique buildings.…”

Section: Introductionmentioning

confidence: 99%

Towards a Service-Oriented Architecture for the Energy Efficiency of Buildings: A Systematic Review

et al. 2021

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Currently, smart buildings generate large amounts of data due to the many devices and equipment available. Hence, buildings implement building management systems (BMSs), which monitor, control, manage and analyze each of these components. However, current BMSs are incapable of managing a massive amount of data (big data) and therefore cannot extract knowledge or make intelligent decisions in quasi real time. In addition, there are serious limitations to integrating BMSs with other services since they generally use proprietary software. In this sense, service-oriented architecture (SOA) is an architectural style that allows one to build distributed systems and provide functionalities such as services to end users or other types of services. Therefore, an SOA has the great advantage of allowing the expansion of the functionalities of BMSs. In fact, there are several studies that address SOAs for building management. However, we have not found any description or systematic analysis in the literature that allows the development of a versatile and interoperable SOA focused on the energy efficiency of buildings and that can integrate massive data analysis features. For these reasons, this study seeks to fill this knowledge gap and, more specifically, to identify and analyze the various software requirements proposed in the literature and the characteristics of big data that allow for improving the energy efficiency of buildings. To this end, we performed an in-depth review of the literature according to the methodology proposed by Kitchenham. As a result of this review, we provide researchers with a specific vision of the requirements and characteristics to consider for software development aimed at the energy efficiency of unique or historic buildings.

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Advanced Fuzzy-Logic-Based Context-Driven Control for HVAC Management Systems in Buildings

Cited by 48 publications

References 56 publications

An Autonomic Cycle of Data Analysis Tasks for the Supervision of HVAC Systems of Smart Building

An Autonomic Cycle of Data Analysis Tasks for the Supervision of HVAC Systems of Smart Building

Evaluating the Adaptability of Reinforcement Learning Based HVAC Control for Residential Houses

Towards a Service-Oriented Architecture for the Energy Efficiency of Buildings: A Systematic Review

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