• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1173-1178
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• 2011

The power generated by wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to predict the changing wind power. In this paper, neural network based wind power prediction scheme which uses wind speed and direction is considered. In order to get a better prediction result, compression function which can be applied to the measurement data is introduced. Empirical data obtained from wind farm located in Kunsan is considered to verify the performance of the compression function.

• Journal of the Semiconductor & Display Technology
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• v.18 no.1
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• pp.123-128
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• 2019

Currently mobile services are on the verge of full commercialization ahead of 5G mobile communication (5G). The first goal could be to preempt the 5G market through realistic media services utilizing VR (Virtual Reality) and AR (Augmented Reality) technologies that users can most easily experience. Basically this movement is based on the advanced development of smart devices and high quality graphics processing computing power of mobile application processors. Accordingly, the importance of mobile GPUs is emerging and the most concern issue becomes a model for predicting the power and performance for smooth operation of high quality mobile contents. In many cases, the performance of mobile GPUs has been introduced in terms of power consumption of mobile GPUs using dynamic voltage and frequency scaling and throttling functions for power consumption and heat management. This paper introduces several studies of mobile GPU performance prediction model with user-friendly methods not like conventional power centric performance prediction models.

• Journal of KIISE
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• v.44 no.9
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• pp.954-965
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• 2017

A stable power supply is very important for the maintenance and operation of the power infrastructure. Accurate power consumption prediction is therefore needed. In particular, a university campus is an institution with one of the highest power consumptions and tends to have a wide variation of electrical load depending on time and environment. For this reason, a model that can accurately predict power consumption is required for the effective operation of the power system. The disadvantage of the existing time series prediction technique is that the prediction performance is greatly degraded because the width of the prediction interval increases as the difference between the learning time and the prediction time increases. In this paper, we first classify power data with similar time series patterns considering the date, day of the week, holiday, and semester. Next, each ARIMA model is constructed based on the classified data set and a daily power consumption forecasting method of the university campus is proposed through the time series cross-validation of the predicted time. In order to evaluate the accuracy of the prediction, we confirmed the validity of the proposed method by applying performance indicators.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.4
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• pp.627-630
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• 2016

Recently, the size of wind farms is becoming larger, and the integration of high wind generation resources into power gird is becoming more important. Due to intermittency of wind generating resources, it is an essential to predict power outputs. In this paper, we introduce the basic concept of curvilinear regression, which is one of the method of wind power prediction. The empirical data, wind farm power output in Jeju Island, is considered to verify the proposed prediction model.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.1
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• pp.49-54
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• 2016

The wind power plants were installed in many places because of the low climate changing effects since 2000. Generally, the wind power plants located in the seaside and the mountainous area and the heights of the windmills are about 40 m~140 m above the ground level. So the noises emitted from the wind power plants propagate far away compared with other environment noise sources like trains and cars noise. Because of these reasons, the noise emitted from the wind power plant is easy to cause the additional social problems like as noise complaints. Under the situation, the ministry of environment has established the guideline to evaluate the environmental effects for the wind power plant. According to the guideline, the noise of the wind power plant has to meet 55 dB(A) at daytime and 45 dB(A) at night in the residential area, which is regulated in the noise and vibration management law. But, it is difficult to estimate the noise emitted from the wind power plant because of the absence of the prediction model of the wind power plant noise. Therefore, the noise prediction model for wind power plants using the regression analysis method is developed in this study. For the development of the model, the sound pressure levels of the wind power plants in Jeju island are measured and the correlations between the sound pressure levels are analyzed. Finally, the prediction equation of the wind power plant noise using by regression analysis method derived. The prediction equation for the wind power plant noise proposed in this study can be useful to evaluate the environmental effects in any wind power plant development district.

• Journal of the Korean Society for Railway
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• v.20 no.4
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• pp.474-481
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• 2017

When designing photovoltaic power plants in Korea, the prediction of photovoltaic power generation at the design phase is carried out using PVSyst, PVWatts (Overseas power generation prediction software), and overseas weather data even if the test site is a domestic site. In this paper, for a comparative study to predict power generation using weather information, domestic photovoltaic power plants in two regions were selected as target sites. PVsyst, which is a commercial power generation forecasting program, was used to compare the accuracy between the predicted value of power generation (obtained using overseas weather information (Meteonorm 7.1, NASA-SSE)) and the predicted value of power generation obtained by the Korea Meteorological Administration (KMA). In addition, we have studied ways to improve the prediction of power generation through comparative analysis of meteorological data. Finally, we proposed a revised solar power generation prediction model that considers climatic factors by considering the actual generation amount.

• Journal of the Society of Disaster Information
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• v.16 no.4
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• pp.842-848
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• 2020

Purpose: It is important to operate safely and economically in obsolescent power plant facilities. Economical operation is related in the balance of the supply and demand. Safety operation predicts the possible risks in the facilities and then, takes measures to the facilities. For the monitoring of the power plant facilities, we needs several kinds of the sensing system. From the sensors data, we can predict the possible risk. Method: We installed the acoustic, vibration, electric and smoke sensors in the power plant facilities. Using the data, we developed 3 kinds of prediction models, such as, demand prediction, plant engine abnormal prediction model, and risk prediction model. Results: Accuracy of the demand prediction model is over 90%. The other models make a stable operation of the system. Conclusion: For the sustainable operation of the obsolescent power plant, we developed 3 kinds of AI prediction models. The model apply to JB company's power plant facilities.

• Journal of Korea Multimedia Society
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• v.25 no.11
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• pp.1572-1581
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• 2022

Energy conversion to renewable energy is being promoted to solve the recently serious environmental pollution problem. Solar energy is one of the promising natural renewable energy sources. Compared to other energy sources, it is receiving great attention because it has less ecological impact and is sustainable. It is important to predict power generation at a future time in order to maximize the output of solar energy and ensure the stability and variability of power. In this paper, solar power generation data and sensor data were used. Using the PCC(Pearson Correlation Coefficient) analysis method, factors with a large correlation with power generation were derived and applied to the GAM(Generalized Additive Model). And the prediction accuracy of the power generation prediction model was judged. It aims to derive efficient solar power generation in the future and improve power generation performance.

• KIEE International Transactions on Power Engineering
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• v.11A no.4
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• pp.15-20
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• 2001

Power system stability is correlated with system structure, disturbances and operating conditions, and power flows on transmission lines are closely related with those conditions. This paper proposes a methodology to identify correlative power flows for power system transient and small-signal stability prediction. In transient stability sense, the Critical Clearing Time is used to select some dominant contingencies, and Transient Stability Prediction index is proposed for the quantitative comparison. For small-signal stability discusses a methodology to identify crucial transmission lines for stability prediction by introducing a sensitivity factor based on eigenvalue sensitivity technique. On-line monitoring of the selected lines enables to predict system stability in real-time. Also, a procedure to make a priority list of monitored transmission lines is proposed. The procedure is applied to a test system, and it shows capabilities of the proposed method.

• Journal of Power Electronics
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• v.13 no.4
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• pp.516-527
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• 2013

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.