• The Journal of the Korea Contents Association
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• v.12 no.7
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• pp.11-20
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• 2012

This paper presents new instruction-level power model for an asynchronous processor, A8051. Even though the proposed model estimates power consumption as instruction level, this model reflects the behavioral features of asynchronous pipeline during the program is executed. Thus, it can effectively enhance the accuracy of power model for an asynchronous embedded processor without significant complexity of power model as well as the increase of simulation time. The proposed power model is based on the implementation of A8051 to reflect the characteristics of power consumption in A8051. The simulation results of the proposed model is compared with that of gate-level synthesized A8051. The proposed power model shows the accuracy of 94% and the simulation time for estimation the power consumption was reduced to 1,600 times.

• Journal of Digital Convergence
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• v.12 no.3
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• pp.211-218
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• 2014

Recently, wind energy is expanding to combination of computing to forecast of wind power generation as well as intelligent of wind powerturbine. Wind power is rise and fall depending on weather conditions and difficult to predict the output for efficient power production. Wind power is need to reliably linked technology in order to efficient power generation. In this paper, distributed power generation forecasts to enhance the predicted and actual power generation in order to minimize the difference between the power of distributed power short-term prediction model is designed. The proposed model for prediction of short-term combining the physical models and statistical models were produced in a physical model of the predicted value predicted by the lattice points within the branch prediction to extract the value of a physical model by applying the estimated value of a statistical model for estimating power generation final gas phase produces a predicted value. Also, the proposed model in real-time National Weather Service forecast for medium-term and real-time observations used as input data to perform the short-term prediction models.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.1
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• pp.17-24
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• 2019

In this paper, we analyzed the effect of power system load model on the short-term voltage stability analysis results. First, we introduced common load models. We also confirmed that some load models can not represent actual system phenomena even if the model parameters are optimized. Also, we studied about the influences of load parameters and regional characteristics of load model on the sort-term voltage stability of KEPCO power system considering the contingency. The results showed that the importance of selecting a load model was confirmed again. And we recognized about it can be understood that it should reflect the load characteristics of the area near the assumed contingency more accurately.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.413-418
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• 2020

It is necessary to manage the prediction accuracy of the machine learning model to prevent the decrease in the performance of the grid network condition prediction model due to overfitting of the initial training data and to continuously utilize the prediction model in the field by maintaining the prediction accuracy. In this paper, we propose an automation technique for maintaining the performance of the model, which increases the accuracy and reliability of the prediction model by considering the characteristics of the power grid state data that constantly changes due to various factors, and enables quality maintenance at a level applicable to the field. The proposed technique modeled a series of tasks for maintaining the performance of the power grid condition prediction model through the application of the workflow management technology in the form of a workflow, and then automated it to make the work more efficient. In addition, the reliability of the performance result is secured by evaluating the performance of the prediction model taking into account both the degree of change in the statistical characteristics of the data and the level of generalization of the prediction, which has not been attempted in the existing technology. Through this, the accuracy of the prediction model is maintained at a certain level, and further new development of predictive models with excellent performance is possible. As a result, the proposed technique not only solves the problem of performance degradation of the predictive model, but also improves the field utilization of the condition prediction model in a complex power grid system.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.12 s.91
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• pp.1147-1151
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• 2004

A novel impedance model is proposed fur the rectangular power plane along with the analytic impedance expression derived from it. The power plane is modeled as a section of a rectangular waveguide with appropriate boundary conditions around its periphery. As a result, the derived impedance expression based on the proposed model has the one-dimensional series form, which is simpler and computationally more efficient than the existing formula based on cavity model of the power plane.

• The KIPS Transactions:PartA
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• v.19A no.3
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• pp.139-146
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• 2012

In the server cluster environment, one of the ways saving energy is to control server's power according to traffic conditions. This is to determine the ON/OFF state of servers according to energy usage of data center and each server. To do this, we need a way to estimate each server's energy. In this paper, we use a software-based power consumption estimation model because it is more efficient than the hardware model using power meter in terms of energy and cost. The traditional software-based power consumption estimation model has a drawback in that it doesn't know well the computing status of servers because it uses only the idle status field of CPU. Therefore it doesn't estimate consumption power effectively. In this paper, we present a CPU field based power consumption estimation model to estimate more accurate than the two traditional models (CPU/Disk/Memory utilization based power consumption estimation model and CPU idle utilization based power consumption estimation model) by using the various status fields of CPU to get the CPU status of servers and the overall status of system. We performed experiments using 2 PCs and compared the power consumption estimated by the power consumption model (software) with that measured by the power meter (hardware). The experimental results show that the traditional model has about 8-15% average error rate but our proposed model has about 2% average error rate.

• Journal of Internet of Things and Convergence
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• v.8 no.3
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• pp.55-61
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• 2022

In order to reduce greenhouse gases, the main culprit of global warming, the United Nations signed the Climate Change Convention in 1992. Korea is also pursuing a policy to expand the supply of renewable energy to reduce greenhouse gas emissions. The expansion of renewable energy development using solar power led to the expansion of wind power and solar power generation. The expansion of renewable energy development, which is greatly affected by weather conditions, is creating difficulties in managing the supply and demand of the power system. To solve this problem, the power brokerage market was introduced. Therefore, in order to participate in the power brokerage market, it is necessary to predict the amount of power generation. In this paper, the prediction system was used to analyze the Yonchuk solar power plant. As a result of applying solar insolation from on-site (Model 1) and the Korea Meteorological Administration (Model 2), it was confirmed that accuracy of Model 2 was 3% higher. As a result of comparative analysis of the DNN and RNN models, it was confirmed that the prediction accuracy of the DNN model improved by 1.72%.

• Journal of Industrial Convergence
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• v.18 no.2
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• pp.69-75
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• 2020

Industrial IoT has much interested in wireless mesh networks (WMNs) due to cost effectiveness and coverage. According to the advance in caching technology, WMNs have been researched to overcome the throughput degradation of multihop environment. However, there is few researches of cache power consumption models for WMNs. In particular, a wired line based cache power consumption model in content-centric networks is not still proper to WMNs. In this paper, we split the amount of cache power from the idle power consumption of CPU, and then the cache hit ratio proportional power consumption model (CHR-model) is proposed. The proposed CHR-model provides more accurate power consumption in WMNs, compared with the conventional cache power efficiency based consumption model (CPE-model). The proposed CHR-model can provide a reference model to improve energy-efficient cache operation for Industrial IoT.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.252-263
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• 2010

The needs of low-power embedded software are being increased. Along with the needs, the studies to predict the power consumption of embedded software are also being increased. Although existing studies for power analysis have been performed in source code-based, these code-based analysis have some shortages of long analysis time and much feedback efforts. Recently some studies of power analysis based on software models are prompted. This paper describes on the model-based approach using UML diagrams in embedded software development process. Specially we focus on the extension of OMG's MARTE Profile to support model-based analysis. The MARTE extension gives the possibility of power analysis using just UML diagrams without any other analysis model in embedded software development.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.4
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• pp.961-970
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• 2017

The differential power analysis calculates the intermediate value related to sensitive information and substitute into the power model to obtain (hypothesized) power consumption. After analyzing the calculated power consumption and measuring power consumption, the secret information value can be obtained. Hamming weight and hamming distance models are most commonly used power consumption model, and the power consumption model is obtained through the modeling technique. If the power consumption model assumed by the actual equipment differs from the power consumption of the actual equipment, the side channel analysis performance is declined. In this paper, we propose a method that records measured power consumption and exploits as power consumption model. The proposed method uses the power consumption at the time when the information (plain text, cipher text, etc.) available in the encryption process. The proposed method does not need template in advance and uses the power consumption measured by the actual equipment, so it accurately reflects the power consumption model of the equipment.. Simulation and experiments show that by using our proposed method, side channel analysis is improved on the existing power modeling method.