The Development of Cloud Energy Management

Cheng, Chin‐Chi; Lee, Dasheng; Wang, Ching Hung; Lin, Shu; Chang, Hou-Chien; Fang, Shang-Te

doi:10.3390/en8054357

Cited by 8 publications

(11 citation statements)

References 42 publications

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“…The Microsoft Azure Platform and MATLAB Simulink are both cloud services as discussed in previous section. [105][106][107][108][109] Distributed AI is employed to transfer commands from the cloud service to local equipment. The MAS was developed.…”

Section: Four-stage Implementation Frameworkmentioning

confidence: 99%

Artificial intelligence implementation framework development for building energy saving

et al. 2020

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In this study, artificial intelligence (AI) control tools were developed to construct an AI implementation framework for energy saving for buildings. Although numerous AI studies related to energy conservation have been conducted, most of them have reported computing algorithms and control effects for single objects. This is the first study to use a framework to integrate five-category AI control tools to execute three-level building energy conservation; the three levels consist of equipment-level control, facility-level control, and whole building energy saving. Energy-saving effects were tested in a real building. The complex three-floor building primarily with a total area of 9072 m 2 serves as an office space and a semiconductor production line. Seventy percent energy consumption comes from air conditioning system and motor power. Twenty percent is lighting system and the other 10% is plug power and office automation equipment. Before implementation, the yearly energy cost reached US$1004339. In 2018, an AI implementation framework was introduced to systematically deploy AI at the site. A total of 47.5%, 37%, and 36.9% of energy was saved at equipment, facility, and whole building levels; up to US$385203 was saved. These energy savings proved the feasibility of the implementation framework. Furthermore, unmet demands of AI studies were met, and an approach to fill the research gap is discussed. K E Y W O R D S artificial intelligence (AI), artificial intelligence implementation framework (AIif), building energy saving, equipment-level control, facility-level control List of Symbols, Abbreviations, and Notation: ω i , weighting coefficients; GS T , the sum of the global similarities between the selected m cases; MV i , the mean difference of the variable i; P j , proportion of the prediction; V 1 , variation of control parameter y; y ic , the neural outputs of variable i for the control; y ip , the neural outputs of variable i for past cases; AI

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Section: Four-stage Implementation Frameworkmentioning

confidence: 99%

Artificial intelligence implementation framework development for building energy saving

et al. 2020

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“…In this sense, cloud computing is a technology to remotely store, monitor, and control any data from any place in the world [13]. This technology can be applied successfully in many areas such as the mobile business, electronic commerce, and data storage service [14], which makes revolutionary improvements to the applications of the information technology (IT) industry [15]. Given its flexible and scalable characteristics, being able to handle large volumes of data, cloud computing can also be applied in smart grid domains [2].…”

Section: Introductionmentioning

confidence: 99%

Management Challenges and Opportunities for Energy Cloud Development and Diffusion

et al. 2020

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The use of emerging technologies such as cloud computing, Internet of Things, and Big Data, is increasing as tools to assist the management of data and information related to energy systems grow. This allows for greater flexibility, scalability of solutions, optimization of energy use, and management of energy devices. In this sense, the objective of this research is to present the basic elements and requirements for the energy cloud and its management and discuss the main management challenges and opportunities for the development and diffusion of the energy cloud. This study was based on a systematic review carried out to identify the elements that compose the energy cloud and what is necessary for its management, and to list the challenges and opportunities that may be explored by researchers and practitioners. The results show that the layout for the energy cloud and its management can be structured in layers and management support blocks’ format. It was found that 70 basic elements make up the main layers and 36 basic elements make up the management support blocks. The findings of this article also provide insights into the technical, scientific, and management development necessary for the evolution of energy systems toward the cloud computing environment.

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“…Reports show that energy consumption has occupied a significant proportion of the total cost of data centers [7]. Energy efficiency is becoming a challenge in data center management [8][9][10][11]. In cloud computing, virtualization is adopted for abstraction and encapsulation such that the underlying infrastructure can be unified as a pool of resources and multiple applications can be executed within isolated virtual machines (VMs) on the same server simultaneously [12].…”

Section: Introductionmentioning

confidence: 99%

An Energy Aware Unified Ant Colony System for Dynamic Virtual Machine Placement in Cloud Computing

2017

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Abstract:Energy efficiency is a significant topic in cloud computing. Dynamic consolidation of virtual machines (VMs) with live migration is an important method to reduce energy consumption. However, frequent VM live migration may cause a downtime of service. Therefore, the energy save and VM migration are two conflict objectives. In order to efficiently solve the dynamic VM consolidation, the dynamic VM placement (DVMP) problem is formed as a multiobjective problem in this paper. The goal of DVMP is to find a placement solution that uses the fewest servers to host the VMs, including two typical dynamic conditions of the assignment of new coming VMs and the re-allocation of existing VMs. Therefore, we propose a unified algorithm based on an ant colony system (ACS), termed the unified ACS (UACS), that works on both conditions. The UACS firstly uses sufficient servers to host the VMs and then gradually reduces the number of servers. With each especial number of servers, the UACS tries to find feasible solutions with the fewest VM migrations. Herein, a dynamic pheromone deposition method and a special heuristic information strategy are also designed to reduce the number of VM migrations. Therefore, the feasible solutions under different numbers of servers cover the Pareto front of the multiobjective space. Experiments with large-scale random workloads and real workload traces are conducted to evaluate the performance of the UACS. Compared with traditional heuristic, probabilistic, and other ACS based algorithms, the proposed UACS presents competitive performance in terms of energy consumption, the number of VM migrations, and maintaining quality of services (QoS) requirements.

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The Development of Cloud Energy Management

Cited by 8 publications

References 42 publications

Artificial intelligence implementation framework development for building energy saving

Artificial intelligence implementation framework development for building energy saving

Management Challenges and Opportunities for Energy Cloud Development and Diffusion

An Energy Aware Unified Ant Colony System for Dynamic Virtual Machine Placement in Cloud Computing

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