• 한국콘텐츠학회논문지
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• 제12권7호
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• pp.11-20
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• 2012

본 논문은 비동기식 프로세서, A8051의 명령어 레벨 소비 전력 모델을 제안한다. 제안된 소비 전력 모델은 명령어 레벨로 프로세서가 소비하는 전력을 예측하지만, 프로그램이 실행되는 동안 비동기식 파이프라인의 동작 특성을 반영한다. 따라서, 제안된 방법은 프로세서 소비 전력 모델의 복잡도와 시뮬레이션 시간의 증가 없이 비동기식 임베디드 프로세서 소비 전력 모델의 정확도를 효과적으로 향상시켰다. 제안된 소비 전력 모델은 A8051의 소비 전력 특성을 반영하여 구현되었고 게이트 레벨의 합성한 결과를 이용한 소비 전력 예측 결과와 비교하여 성능 평가를 수행하였다. 제안된 소비 전력 모델은 게이트 레벨의 소비 전력예측 결과와 비교하여 94%의 정확도를 보였고, 1,600 배 이상 시뮬레이션 시간을 단축하였다.

• Nuclear Engineering and Technology
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• 제49권1호
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• pp.134-140
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• 2017

A well-performed core power control to track load changes is crucial in pressurized water reactor (PWR) nuclear power stations. It is challenging to keep the core power stable at the desired value within acceptable error bands for the safety demands of the PWR due to the sensitivity of nuclear reactors. In this paper, a state-space model predictive control (MPC) method was applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, the MPC model, and quadratic programming (QP). The mathematical models of the reactor core were based on neutron dynamic models, thermal hydraulic models, and reactivity models. The MPC model was presented in state-space model form, and QP was introduced for optimization solution under system constraints. Simulations of the proposed state-space MPC control system in PWR were designed for control performance analysis, and the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.

• Industrial Engineering and Management Systems
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• 제15권2호
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• pp.148-155
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• 2016

Existing power plants may consume significant amounts of fuel and require high operating costs, partly because of poor electrical power output estimates. This paper suggests a continuous conditional random field (C-CRF) model to predict more precisely the full-load electrical power output of a base load operated combined cycle power plant. We introduce three feature functions to model association potential and one feature function to model interaction potential. Together, these functions compose the C-CRF model, and the model is transformed into a multivariate Gaussian distribution with which the operation parameters can be modeled more efficiently. The performance of our model in estimating power output was evaluated by means of a real dataset and our model outperformed existing methods. Moreover, our model can be used to estimate confidence intervals of the predicted output and calculate several probabilities.

• 산업경영시스템학회지
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• 제42권1호
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• pp.129-136
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• 2019

In this study, we proposed a model for forecasting power energy demand by investigating how outside temperature at a given time affected power consumption and. To this end, we analyzed the time series of power consumption in terms of the power spectrum and found the periodicities of one day and one week. With these periodicities, we investigated two time series of temperature and power consumption, and found, for a given hour, an approximate linear relation between temperature and power consumption. We adopted an exponential smoothing model to examine the effect of the linearity in forecasting the power demand. In particular, we adjusted the exponential smoothing model by using the variation of power consumption due to temperature change. In this way, the proposed model became a mixture of a time series model and a regression model. We demonstrated that the adjusted model outperformed the exponential smoothing model alone in terms of the mean relative percentage error and the root mean square error in the range of 3%~8% and 4kWh~27kWh, respectively. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric energy together with the outside temperature.

• ETRI Journal
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• 제37권1호
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• pp.157-164
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• 2015

As GPU power consumption in smartphones increases with more advanced graphic performance, it becomes essential to estimate GPU power consumption accurately. The conventional GPU power model assumes, simply, that a GPU consumes constant power when turned on; however, this is no longer true for recent smartphone GPUs. In this paper, we propose an accurate GPU power model for smartphones, considering newly adopted dynamic voltage and frequency scaling. For the proposed GPU power model, our evaluation results show that the error rate for system power estimation is as low as 2.9%, on average, and 4.6% in the worst case.

• 전기학회논문지P
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• 제63권4호
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• pp.307-311
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• 2014

Due to the increasing of power consumption, it is difficult to construct accurate prediction model for daily peak power demand. It is very important work to know power demand in next day for manager and control power system. In this research, we develop a daily peak power demand prediction method considering of characteristics of day of week. The proposed method is composed of liner model based on AR model and nonlinear model based on ELM to resolve the limitation of a single model. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 A
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• pp.430-432
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• 2004

The standard power system model is needed to test a transmission line protective relay There are two methods to develop a power system model for transmission line protection. First method is based on characteristic power system model, and second method is based on functional power system model. This paper presents a standard power system model for performance test of transmission line protective relay, where the power system model is based on the two methods. And this model is simulated by using RTDS to test a protective relay.

• 한국IT서비스학회지
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• 제21권4호
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• pp.91-103
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• 2022

This study proposes a model for predicting contracted power using electric power data collected in real time from convenience stores nationwide. By optimizing the prediction model using machine learning, it will be possible to predict the contracted power required to renew the contract of the existing convenience store. Contracted power is predicted through the XGBoost regression model. For the learning of XGBoost model, the electric power data collected for 16 months through a real-time monitoring system for convenience stores nationwide were used. The hyperparameters of the XGBoost model were tuned using the GridesearchCV, and the main features of the prediction model were identified using the xgb.importance function. In addition, it was also confirmed whether the preprocessing method of missing values and outliers affects the prediction of reduced power. As a result of hyperparameter tuning, an optimal model with improved predictive performance was obtained. It was found that the features of power.2020.09, power.2021.02, area, and operating time had an effect on the prediction of contracted power. As a result of the analysis, it was found that the preprocessing policy of missing values and outliers did not affect the prediction result. The proposed XGBoost regression model showed high predictive performance for contract power. Even if the preprocessing method for missing values and outliers was changed, there was no significant difference in the prediction results through hyperparameters tuning.

• 경영과학
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• 제33권1호
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• pp.35-47
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• 2016

Generation of electricity using wind power has received considerable attention worldwide in recent years mainly due to its minimal environmental impact. However, volatility of wind power production causes additional problems to provide reliable electricity to an electrical grid regarding power system operations, power system planning, and wind farm operations. Those problems require appropriate stochastic models for the electricity generation output of wind power. In this study, we review previous literatures for developing the stochastic model for the wind power generation, and propose a systematic procedure for developing a stochastic model. This procedure shows a way to build an ARIMA model of volatile wind power generation using historical data, and we suggest some important considerations. In addition, we apply this procedure into a case study for a wind farm in the Republic of Korea, Shinan wind farm, and shows that our proposed model is helpful for capturing the volatility of wind power generation.

• 전기학회논문지
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• 제58권5호
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• pp.918-922
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• 2009

The power expansion planning is large and capital intensive capacity planning. In the past, the expansion planning was established with the proper supply reliability in order to minimize social cost. However, the planning cannot use cost minimizing objective function in the power markets with many market participants. This paper proposed the power expansion planning process in the power markets. This system is composed of Regulator and GENCO's model. Regulator model used multi-criteria decision making rule. GENCO model is very complex problem. Thus, this system transacted the part by several scenario assuming GENCO model.