Fog BEMS: An Agent-Based Hierarchical Fog Layer Architecture for Improving Scalability in a Building Energy Management System

Na, Ui-Kyun; Lee, Eun-Kyu

doi:10.3390/su12072831

Cited by 11 publications

(7 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Building Energy Management Systems (BEMS) are poised to undergo significant advancements and transformations in the coming years, driven by technological innovations and the increasing focus on sustainability and energy efficiency (Groumpos & Mpelogianni, 2020, Hannan, et. al., 2023, Na & Lee, 2020, Nguyen, et. al., 2020.…”

Section: Future Trends and Opportunities In Building Energy Managemen...mentioning

confidence: 99%

A comprehensive review of Building Energy Management Systems (BEMS) for Improved Efficiency

Wags Numoipiri Digitemie,

Ifeanyi Onyedika Ekemezie

2024

World J. Adv. Res. Rev.

View full text Add to dashboard Cite

Building Energy Management Systems (BEMS) play a crucial role in enhancing energy efficiency and sustainability in buildings. This abstract provides a comprehensive review of BEMS, focusing on its components, benefits, challenges, and future trends. BEMS is a centralized system that monitors and controls building services, such as heating, ventilation, air conditioning, lighting, and other systems, to improve energy efficiency and occupant comfort. The key components of BEMS include sensors, controllers, communication networks, and user interfaces. These components work together to collect data on building performance, analyze energy consumption patterns, and optimize system operation. One of the primary benefits of BEMS is its ability to reduce energy consumption and costs. By monitoring and controlling building systems based on occupancy schedules and environmental conditions, BEMS can significantly reduce energy waste. Additionally, BEMS can improve occupant comfort and productivity by maintaining optimal indoor environmental conditions. Despite its benefits, BEMS faces several challenges, including high upfront costs, complexity of installation, and integration issues with existing building systems. However, advancements in technology, such as the Internet of Things (IoT) and cloud computing, are addressing these challenges and making BEMS more accessible and cost-effective. Looking ahead, the future of BEMS is promising, with continued advancements in technology driving its adoption and integration into smart building systems. The integration of artificial intelligence and machine learning algorithms into BEMS is expected to further improve its energy-saving capabilities and enhance building performance. In conclusion, BEMS is a key technology for improving energy efficiency and sustainability in buildings. By leveraging its components and capabilities, BEMS can help reduce energy consumption, lower costs, and create healthier and more comfortable indoor environments.

show abstract

Section: Future Trends and Opportunities In Building Energy Managemen...mentioning

confidence: 99%

A comprehensive review of Building Energy Management Systems (BEMS) for Improved Efficiency

Wags Numoipiri Digitemie,

Ifeanyi Onyedika Ekemezie

2024

World J. Adv. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…More end devices are deployed and linked to the cloud, including IoT devices, power equipment, and smartphone users. Fog-computing processes user data in real-time with distributed resources and uses local contextual knowledge [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Auto-Scaled Service-Cloud-Based Predictive Workload Modeling and Analysis for Smart Campus System

et al. 2021

View full text Add to dashboard Cite

The internet of things is an emerging technology used in cloud computing and provides many services of the cloud. The cloud services users mostly suffer from service delays and disruptions due to service cloud resource management based on vertical and horizontal scalable systems. Adding more resources to a single cloud server is called vertical scaling, and an increasing number of servers is known as horizontal scaling. The service-bursts significantly impact the vertical scaled environment where the scaleup degrades the service quality and users' trust after reaching the server's maximum capacity. Besides, the horizontally scaled environment, though being resilient, is cost-inefficient. It is also hard to detect and manage bursts online to sustain application efficiency for complex workloads. Burst detection in real-time workloads is a complicated issue because even in the presence of auto-scaling methods, it can dramatically degrade the application's efficiency. This research study presents a new bursts-aware auto-scaling approach that detects bursts in dynamic workloads using resource estimation, decision-making scaling, and workload forecasting while reducing response time. This study proposes a hybrid auto-scaled service cloud model that ensures the best approximation of vertical and horizontal scalable systems to ensure Quality of Service (QoS) for smart campus-based applications. This study carries out the workload prediction and auto-scaling employing an ensemble algorithm. The model pre-scales the scalable vertical system by leveraging the service-load predictive modeling using an ensemble classification of defined workload estimation. The prediction of the upcoming workload helped scale-up the system, and auto-scaling dynamically scaled the assigned resources to many users' service requests. The proposed model efficiently managed service-bursts by addressing load balancing challenges through horizontal auto-scaling to ensure application consistency and service availability. The study simulated the smart campus environment model to monitor the timestamped diverse service-requests appearing with different workloads.INDEX TERMS Auto-scaling, cloud computing, horizontal scalability, internet of things, predictive modeling, quality of service (QoS), smart campus, vertical scalability, workloads

show abstract

“…Smart grids are the next application with PHC references [135][136][137][138][139][140][141][142][143][144][145][146]. The authors in ref.…”

Section: Smart Gridsmentioning

confidence: 99%

A Review of Parallel Heterogeneous Computing Algorithms in Power Systems

et al. 2021

View full text Add to dashboard Cite

The power system expansion and the integration of technologies, such as renewable generation, distributed generation, high voltage direct current, and energy storage, have made power system simulation challenging in multiple applications. The current computing platforms employed for planning, operation, studies, visualization, and the analysis of power systems are reaching their operational limit since the complexity and size of modern power systems results in long simulation times and high computational demand. Time reductions in simulation and analysis lead to the better and further optimized performance of power systems. Heterogeneous computing—where different processing units interact—has shown that power system applications can take advantage of the unique strengths of each type of processing unit, such as central processing units, graphics processing units, and field-programmable gate arrays interacting in on-premise or cloud environments. Parallel Heterogeneous Computing appears as an alternative to reduce simulation times by optimizing multitask execution in parallel computing architectures with different processing units working together. This paper presents a review of Parallel Heterogeneous Computing techniques, how these techniques have been applied in a wide variety of power system applications, how they help reduce the computational time of modern power system simulation and analysis, and the current tendency regarding each application. We present a wide variety of approaches classified by technique and application.

show abstract

Fog BEMS: An Agent-Based Hierarchical Fog Layer Architecture for Improving Scalability in a Building Energy Management System

Cited by 11 publications

References 54 publications

A comprehensive review of Building Energy Management Systems (BEMS) for Improved Efficiency

A comprehensive review of Building Energy Management Systems (BEMS) for Improved Efficiency

Hybrid Auto-Scaled Service-Cloud-Based Predictive Workload Modeling and Analysis for Smart Campus System

A Review of Parallel Heterogeneous Computing Algorithms in Power Systems

Contact Info

Product

Resources

About