• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1892-1912
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• 2022

With the massive demand and growth of cloud computing, virtualization plays an important role in providing services to end-users efficiently. However, with the increase in services over Cloud Computing, it is becoming more challenging to manage and run multiple Virtual Machines (VMs) in Cloud Computing because of excessive power consumption. It is thus important to overcome these challenges by adopting an efficient technique to manage and monitor the status of VMs in a cloud environment. Reduction of power/energy consumption can be done by managing VMs more effectively in the datacenters of the cloud environment by switching between the active and inactive states of a VM. As a result, energy consumption reduces carbon emissions, leading to green cloud computing. The proposed Efficient Dynamic VM Scheduling approach minimizes Service Level Agreement (SLA) violations and manages VM migration by lowering the energy consumption effectively along with the balanced load. In the proposed work, VM Scheduling for Efficient Dynamically Migrated VM (VMS-EDMVM) approach first detects the over-utilized host using the Modified Weighted Linear Regression (MWLR) algorithm and along with the dynamic utilization model for an underutilized host. Maximum Power Reduction and Reduced Time (MPRRT) approach has been developed for the VM selection followed by a two-phase Best-Fit CPU, BW (BFCB) VM Scheduling mechanism which is simulated in CloudSim based on the adaptive utilization threshold base. The proposed work achieved a Power consumption of 108.45 kWh, and the total SLA violation was 0.1%. The VM migration count was reduced to 2,202 times, revealing better performance as compared to other methods mentioned in this paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.285-289
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• 2020

The configuration of hierarchical wireless networks is provided to support diverse network environments. In the base station, two system state can be basically considered for the operation management so that the state transition may be occurred between active and sleep modes. Hence, to reduce energy consumption the system operation management of the low power should be considered to the base station system. In this paper we consider the analytical model of Discontinuous Reception (DRX) to investigate the system management. We provide the analysis scheme of base station system by the DRX model, and the low power factor would be investigated for the energy consumption. We also use the finite-state Markov system model that in a system state period the wireless resource request and the operation of service call arrival interval is considered to numerically analyze the performance of energy saving operations of base station.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.6
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• pp.37-48
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• 2005

The efficiency of power supply circuits such as DC-DC converters and batteries varies on the trend of the power consumption because their efficiencies are not fixed. To analyze the efficiency of power supply circuits, we need the temporal behavior of the power consumption of the loads, which is dependent on the activity factors of the devices during the operation. Since it is not easy to model every detail of those factors, one of the most accurate power consumption analyses of power supply circuits is measurement of a real system, which is expensive and time consuming. In this paper, we introduce an active load emulator for embedded systems which is capable of power measurement, logging, replaying and synthesis. We adopt a pattern recognition technique for data compression in that long-term behaviors of power consumption consist of numbers of repetitions of short-term behaviors, and the number of short-term behaviors is generally limited to a small number. We also devise a heterogeneous structure of active load elements so that low-speed, high-current active load elements and high-speed, low-current active load elements may emulate large amount and fast changing power consumption of digital systems. For the performance evaluation of our load emulator, we demonstrate power measurement and emulation of a hard drive. As an application of our load emulator, it is used for the analysis of a DC-DC converter efficiency and for the verification of a low-power frequency scaling policy for a real-time task.

• Journal of Information Processing Systems
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• v.2 no.1
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• pp.44-47
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• 2006

One of the critical issues in a sensor network concerns the detection of changes in data streams. Recently presented change detection schemes primarily use a sliding window model to detect changes. In such a model, a distance function is used to compare two sliding windows. Therefore, the performance of the change detection scheme is greatly influenced by the distance function. With regard to sensor nodes, however, energy consumption constitutes a critical design concern because the change detection scheme is implemented in a sensor node, which is a small battery-powered device. In this paper, we present a comparative study of various distance functions in terms of execution time, energy consumption, and detecting accuracy through simulation of speech signal data. The simulation result demonstrates that the Euclidean distance function has the highest performance while consuming a low amount of power. We believe our work is the first attempt to undertake a comparative study of distance functions in terms of execution time, energy consumption, and accuracy detection.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.419-430
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• 2020

This paper investigated methods to improve the forecasting accuracy of the electricity consumption prediction model. Currently, the demand for electricity has continuously been rising more than ever. Since the industrial sector uses more electricity than any other sectors, the importance of a more precise forecasting model for manufacturing sites has been highlighted to lower the excess energy production. We propose a double encoder-decoder model, which uses two separate encoders and one decoder, in order to adapt both long-term and short-term data for better forecasts. We evaluated our proposed model on our electricity power consumption dataset, which was collected in a manufacturing site of Sehong from January 1st, 2019 to June 30th, 2019 with 1 minute time interval. From the experiment, the double encoder-decoder model marked about 10% reduction in mean absolute error percentage compared to a conventional encoder-decoder model. This result indicates that the proposed model forecasts electricity consumption more accurately on manufacturing sites compared to an encoder-decoder model.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.14 no.4
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• pp.1-6
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• 2018

In this study, the energy consumption of the typical floor was compared by the total energy comsumption of the building in highrise building. In gerneral, many researchers are studying on the typical floor in highrise buildings for avoiding complexity in energy simulation. But few papers are studied on energy consumption along the floors. In the model bulding, the energy consumption data were acquired by BEMS system in 2011. According the data, the total net energy consumption was $193.99kWh/m^2$ for all area and the total net energy consumption was $247.61kWh/m^2$ for HVACR area. The total electricity and gas energy are used 47.7% for heating and cooling, 33.5% for lighting and plug, 12.9% for conveyance power and 5.9% for restaurant. In comparison of only ground floor, amount of energy consumption in the lobby is 10%, and 90% of total energy consumption is used in the typical floor. For this result, energy simulation on the typical floor is acceptable for calculating the total energy consumption in the highrise building.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.7
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• pp.3152-3159
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• 2012

This paper presents the new instruction-level power model for an asynchronous processor. Until now, the various power models for estimating the power dissipation of embedded processor in SoC are proposed. Since all of them are target to the synchronous processors, the accuracy is questionable when we apply those power models to the asynchronous processor in SoC. To solve this problem, we present new power model for an asynchronous processor by reflecting the behavioral features of an asynchronous circuit. The proposed power model is verified using an implementation of asynchronous processor, A8051. The simulation results of the proposed model is compared with the measurement result of gate-level synthesized A8051. The proposed power model shows the accuracy of 90.7% and the simulation time for estimation the power consumption was reduced to 1,900 times.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.1
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• pp.153-161
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• 2004

A vehicle fuel economy is one of the most important issues in view of environmental regulation and customer's needs. In order to improve the vehicle fuel economy, great efforts has been carried out on the components bases. However, systematic analysis of vehicle fuel consumption is necessary for the further improvement of vehicle fuel economy. In this paper, a methodology for the breakdown of vehicle fuel consumption was studied and proposed for systematic analysis of the vehicle fuel economy. The energy equation for the vehicle power train was set up for the analysis of the vehicle fuel economy and simplified to be calculated or estimated using the measured data in a vehicle. The amount of fuel that was used in vehicle components under arbitrary driving conditions was quantified.

• Architectural research
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• v.21 no.1
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• pp.21-29
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• 2019

Recent years in Korea, some new developed buildings are only using electricity as power for heating, cooling, bathing and even cooking which means except electricity, there is no natural gas or other kinds of energy used in such kind of building. In vehicle industry area, scientists already invented electric vehicle as an environment friendly vehicle; after that, in architecture design and construction field, buildings only using electricity appeared; the curiosity of the environment impact of energy consumption by such kind of building lead me to do this research. In general, electricity is known as a clean energy resource reasoned by it is noncombustible energy resource; however, although there is no environmental pollution by using electricity, electricity generation procedure in power plant may cause huge amount of environment pollution; especially, electricity generation from combusting coal in power plant could emit enormous air pollutants to the air. In this research, the yearly amount of air pollution by energy using under traditional way in research target building that is using natural gas for heating, bathing and cooking and electricity for lighting, equipment and cooling is compared with yearly amount of air pollution by only using electricity as power in the building; result shows that building that only uses electricity emits much more air pollutants than uses electricity and natural gas together in the building. According to the amount of air pollutants comparison result between two different energy application types in the building, residential SOFC (Solid oxide fuel cell) is simulated to apply in this building for decreasing environment pollution of the building; furthermore, high load factor could lead high efficiency of SOFC, in the scenario of simulating applying SOFC in the building, SOFC is shared by two or three households in spring and autumn to increase efficiency of the SOFC. In sum, this research is trying to demonstrate electricity is a conditioned environment friendly energy resource; in the meanwhile, SOFC is simulated efficiently applying in the building only using electricity as power to decrease the large amount of air pollutants by energy using in the building. Energy consumption of the building is analyzed by calibrated commercial software Design Builder; the calibrated mathematical model of SOFC is referred from other researcher's study.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.715-724
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• 2023

Solar energy forecasting is essential for (1) power system planning, management, and operation, requiring accurate predictions. It is crucial for (2) ensuring a continuous and sustainable power supply to customers and (3) optimizing the operation and control of renewable energy systems and the electricity market. Recently, research has been focusing on developing solar energy forecasting models that can provide daily plans for power usage and production and be verified in the electricity market. In these prediction models, various data, including solar energy generation and climate data, are chosen to be utilized in the forecasting process. The most commonly used climate data (such as temperature, relative humidity, precipitation, solar radiation, and wind speed) significantly influence the fluctuations in solar energy generation based on weather conditions. Therefore, this paper proposes a hybrid forecasting model by combining the strengths of the Prophet model and the GRU model, which exhibits excellent predictive performance. The forecasting periods for solar energy generation are tested in short-term (2 days, 7 days) and medium-term (15 days, 30 days) scenarios. The experimental results demonstrate that the proposed approach outperforms the conventional Prophet model by more than twice in terms of Root Mean Square Error (RMSE) and surpasses the modified GRU model by more than 1.5 times, showcasing superior performance.