• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.3
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• pp.290-295
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• 2015

Analysis and verification of reactive power compensator (RPC) for ITER pulsed power electric network (PPEN) are described in this paper. The RPC system is rated for a nominal power of 250 Mvar necessary to comply with the allowable reactive power limit value from the grid 200 Mvar. This system is currently under construction and is based on static var compensation technology with a thyristor-controlled reactor and a harmonic filter. The RPC minimizes reactive power from grid using prediction of reactive power consumption of AC-DC converters. The feasibility of the reactive power compensation was verified by assembling a real controller and implementing ITER PPEN in the real time digital simulator for the hardware-in-loop facility. When maximum reactive power is reached, grid voltage is stabilized and maximum reactive power decreased from 120 Mvar to 40 Mvar via the reactive power prediction method.

• Journal of the Korean Institute of Navigation
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• v.24 no.3
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• pp.147-155
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• 2000

In this paper, we propose the Chaos Fuzzy controller to analyze the chaotic character of time series obtained from the specific plant and to predict the short-term for power consumption of the plant using the Fuzzy controller. We compared the predicted data with the active ones and checked the error generated by them after we time series of supplied power to the proposed controller. As a result of the simulation, we obtained a admirable consequence that the proposed controller can be advanced through various and accurate data acquisition, and continuous analysis of the resident and industrial environment.

• The Journal of Bigdata
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• v.6 no.1
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• pp.127-132
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• 2021

Korea is both a resource-poor country and a energy-consuming country. In addition, the use and dependence on electricity is very high, and more than 20% of total energy use is consumed in buildings. As research on deep learning and machine learning is active, research is underway to apply various algorithms to energy efficiency fields, and the introduction of building energy management systems (BEMS) for efficient energy management is increasing. In this paper, we constructed a database based on energy usage by device per household directly collected using smart plugs. We also implement algorithms that effectively analyze and predict the data collected using RNN and LSTM models. In the future, this data can be applied to analysis of power consumption patterns beyond prediction of energy consumption. This can help improve energy efficiency and is expected to help manage effective power usage through prediction of future data.

• Journal of the korean Society of Automotive Engineers
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• v.3 no.3
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• pp.49-57
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• 1981

A cycle simulation of 4 cycle spark ignition engine using methanol-water blend as a fuel has been developed for study of prediction of power, specific fuel consumption, mean effective pressure and thermal efficiency. One-dimensional flow model for intake process and thermodynamic model for combustion process were selected. After, performance test was made with conventional engine which was modified in consideration of fuel properties. And computational results by simulation have been compared with experimental results. As the agreement between computational and experimental results was good, prediction of engine performance by was possible.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.6
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• pp.97-106
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• 2011

Both an optimal design of the engine operating strategy and fuel economy prediction technique for a HEV under the vehicle driving condition are very crucial for the development of vehicle fuel economy performance. Thus, in this study, engine operating characteristics of PRIUS III were analyzed with vehicle running conditions and the correlations between vehicle tractive power and fuel consumption were introduced. As a result, fuel economy performance of PRIUS III with various test modes were predicted and verified. Errors of predicted fuel economy were between -5% and -1%.

• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.307-316
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• 2009

In this paper, the spatiotemporal data mining methodology for detecting a cycle of power consumption pattern with the change of time and spatial was proposed, and applied to the power consumption data collected by GIS-AMR system with an aim to use its resulting knowledge in real world applications. First, partial clustering method was applied for cluster analysis concerned with the aim of customer's power consumption. Second, the patterns of customer's power consumption data which contain time and spatial attribute were detected by 3D cube mining method. Third, using the calendar pattern mining method for detection of cyclic patterns in the various time domains, the meanings and relationships of time attribute which is previously detected patterns were analyzed and predicted. For the evaluation of the proposed spatiotemporal data mining, we analyzed and predicted the power consumption patterns included the cycle of time and spatial feature from total 266,426 data of 3,256 customers with high power consumption from Jan. 2007 to Apr. 2007 supported by the GIS-AMR system in KEPRI. As a result of applying the proposed analysis methodology, cyclic patterns of each representative profiles of a group is identified on time and location.

• Journal of IKEEE
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• v.23 no.1
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• pp.134-142
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• 2019

As the development of internet of energy (IoE) technologies and spread of various electronic devices have diversified patterns of energy consumption, the reliability of demand prediction has decreased, causing problems in optimization of power generation and stabilization of power supply. In this study, we propose a deep learning method, 1-Dimention-Convolution and Bidirectional Long Short-Term Memory (1D-ConvBLSTM), that combines a convolution neural network (CNN) and a Bidirectional Long Short-Term Memory(BLSTM) for highly reliable demand forecasting by effectively extracting the energy consumption pattern. In experimental results, the demand is predicted with the proposed deep learning method for various number of learning iterations and feature maps, and it is verified that the test data is predicted with a small number of iterations.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.231-234
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• 2008

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in load demand data. The main aim of our work is to forecast customers' contract information from capacity of daily power consumption patterns. According to the result, we try to evaluate the contract information's suitability. The proposed our approach consists of three stages: (i) data preprocessing: noise or outlier is detected and removed (ii) cluster analysis: SOMs clustering is used to create load patterns and the representative load profiles and (iii) classification: we applied the K-NNs classifier in order to predict the customers' contract information base on power consumption patterns. According to the our proposed methodology, power load measured from AMR(automatic meter reading) system, as well as customer indexes, were used as inputs. The output was the classification of representative load profiles (or classes). Lastly, in order to evaluate KNN classification technique, the proposed methodology was applied on a set of high voltage customers of the Korea power system and the results of our experiments was presented.

• Journal of Ocean Engineering and Technology
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• v.28 no.2
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• pp.111-118
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• 2014

In drilling process of oil wells, the drilling fluid such as mud keeps the drill bit cool and clean during drilling, with suspending drill cuttings and lubricating a drill bit. In this paper, a commercial CFD package(ANSYS Fluent 15.0) was used to solve the hydrodynamic force and evaluate mud mixing time in the mud mixing tank on offshore drilling platforms. Prediction of power consumption in co-rotating and counter-rotating models has been compared with results of Nagata's correlation equation. This research shows the hydrodynamic effect inside the two phase mud mixing tank according to rotating directions(co-rotating and counter-rotating). These results, we can conclude that the co-rotating direction of the two shafts with mixing blade in the mud mixing tank can be a preferable in power consumption and mixing time reduction.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.25 no.12
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• pp.1730-1738
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• 2001

This paper presents an overview of testing and analyzing field performance of a centrifugal chiller which has a rated capacity of 200 RT(703 kW). Field data of a chiller installed in the cleanroom research building of KIST has been collected far performance analysis. The operating data included start-up, shut-down, and quasi-static state where cooling capacity and compressor power consumption varied cyclically. It was found that the steady-state thermodynamic model could be applied to relate the cooling capacity and COP under quasi-static conditions. The results led to finding the required cooling load pattern and a possible energy saving method. This study provides a method of evaluating performance of a large capacity centrifugal chiller in which field test is necessary.