• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.4
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• pp.96-101
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• 1999

Load model is very important to improve accuracy of stability analysis and load flow study in power systems. A power system bus is composed by various loads, and loads have different power consumption due to voltage/frequency changing. Thus the effect of voltage/frequency changing must he considered to load mxleling. In this research, ANN was used to construct component load moddel for more accurate load mxleling. Typical residential load was selected, and characteristics exrerimented on voltage/frequency changing. Acquired data used to construct the component ANN model, and aggregation method of component load model was presented based on component load model and composition rate. Furthennore, it's transfomlation method to the mathematical load model to he used at the traditional power system analysis soft wares was also presented.sented.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.585-588
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• 2019

Predictions of Energy Consumption for Industries gain an important place in energy management and control system, as there are dynamic and seasonal changes in the demand and supply of energy. This paper presents and discusses the predictive models for energy consumption of the steel industry. Data used includes lagging and leading current reactive power, lagging and leading current power factor, carbon dioxide (tCO2) emission and load type. In the test set, four statistical models are trained and evaluated: (a) Linear regression (LR), (b) Support Vector Machine with radial kernel (SVM RBF), (c) Gradient Boosting Machine (GBM), (d) random forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the prediction efficiency of regression designs. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.14-21
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• 2020

In order to provide confidential services between two communicating parties, block data encryption using a symmetric secret key is applied. A power analysis attack on a cryptosystem is a side channel-analysis method that can extract a secret key by measuring the power consumption traces of the crypto device. In this paper, we propose an attack model that can recover the secret key using a power analysis attack based on a deep learning convolutional neural network (CNN) algorithm. Considering that the CNN algorithm is suitable for image analysis, we particularly adopt the recurrence plot (RP) signal processing method, which transforms the one-dimensional power trace into two-dimensional data. As a result of executing the proposed CNN attack model on an XMEGA128 experimental board that implemented the AES-128 encryption algorithm, we recovered the secret key with 22.23% accuracy using raw power consumption traces, and obtained 97.93% accuracy using power traces on which we applied the RP processing method.

• Journal of the Korean Society for Railway
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• v.14 no.2
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• pp.167-173
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• 2011

This paper considers the problem of revising train departure time to reduce electric power consumption of mass rapid transit (MRT) railways. The motion of a train running between stations is divided into three phases: traction, coasting, and deceleration phases. The traction phase requires high electric power to operate MRT railways. In the coasting phase, the train moves stably by consuming little or no power. The deceleration phase is a braking mode and produces some electric power called regenerated brake power owing to inertia force for the train generated In the traction and coasting phases. The regenerative energy can be used by other accelerating trains within a specific range from the train and thereby the power consumptions of train can be reduced. We developed a mixed integer programming model to solve the problem. To validate the suggested model, a computational experiment was conducted using real data from Korea Metropolitan Subway.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.253-259
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• 2017

Recent development in science and technology has modernized the weapon system of ROKN (Republic Of Korea Navy). Although the cost of purchasing, operating and maintaining the cutting-edge weapon systems has been increased significantly, the national defense expenditure is under a tight budget constraint. In order to maintain the availability of ships with low cost, we need accurate demand forecasts for spare parts. We attempted to find consumption pattern using data mining techniques. First we gathered a large amount of component consumption data through the DELIIS (Defense Logistics Intergrated Information System). Through data collection, we obtained 42 variables such as annual consumption quantity, ASL selection quantity, order-relase ratio. The objective variable is the quantity of spare parts purchased in f-year and MSE (Mean squared error) is used as the predictive power measure. To construct an optimal demand forecasting model, regression tree model, randomforest model, neural network model, and linear regression model were used as data mining techniques. The open software R was used for model construction. The results show that randomforest model is the best value of MSE. The important variables utilized in all models are consumption quantity, ASL selection quantity and order-release rate. The data related to the demand forecast of spare parts in the DELIIS was collected and the demand for the spare parts was estimated by using the data mining technique. Our approach shows improved performance in demand forecasting with higher accuracy then previous work. Also data mining can be used to identify variables that are related to demand forecasting.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.218-219
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• 2006

This paper is about modeling on 1500V DC electric railroad system. Electric railroad systems have peculiar characteristics against other electric system. The characteristics arc that the railroad systems have electric vehicle loads which are power-varying and location-varying with time. Because of this load characteristic, the electric railroad system modeling which reflects its own characteristics on EMTDC simulation could not be achieved. However, to reflect load characteristic on EMTDC, this paper suggests electric railroad system modeling by using TPS (Train Performance Simulator) that was developed in Korea Railroad Research Institute. A TPS program has various kinds of input data, such as operation condition, vehicle condition, and power system condition. By these data, TPS calculates mechanical power consumption and location, especially it decide electric power consumption on the basis of the fact that consumed electric and mechanical power are equal. Moreover, on this paper, movement of vehicle is reflected on EMTDC simulation as variation of feeder impedance. Also, an electric vehicle load is modeled as time-varying constant power load model.

• The Transactions of the Korea Information Processing Society
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• v.7 no.8
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• pp.2520-2527
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• 2000

An improved transition density propagation method for power estimation is proposed. The power estimation for the zero delay model is a proper criteria for the.lower boutldlIry for power consumption. A transition propagation method, including the zero delay model as a lower boundary for power stimation was studied. However, there were some redundancy factors in the process of transition density propagation. Hence this paper will explore the transition density propagation behavior to eliminate the redundancy factors and present theirriprQved estimation methodology for the signal transition density. The experiments show that the proposed method has comparably better estimation accuracy than the conventional methods.

• Journal of Distribution Science
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• v.22 no.8
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• pp.87-98
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• 2024

Purpose: This study aimed to examine the influence of private label cues, including store image, product design, price promotion, and origin image, on consumers repurchase intention by mediating consumption value from a distribution perspective. Additionally, it explored nationality's moderating role in the relationship between consumption value and repurchase intention. Research design, data and methodology: Drawing on the SOR model, data were collected from 246 consumers who had purchased private-label products in the past month. Structural equation modeling analysis was employed to test hypotheses using AMOS and SPSS. Results: Findings revealed that cues significantly impact consumers' perception of consumption value, influencing repurchase intention. Price promotion directly affected repurchase intention, while other cues indirectly influenced it through consumption value mediation. Nationality moderated the relationship between consumption value and repurchase intention, with Korean consumers showing a higher propensity to repurchase than Chinese consumers. Conclusions: Theoretical implications of the study contributed to understanding consumer behavior by confirming the impact of private label cues, elucidating their differential effects on repurchase intention, and integrating theoretical frameworks. Managerial implications underscored the significance of leveraging cues to enhance consumption value perceptions, tailoring marketing strategies to accommodate cultural nuances, and utilizing cues to bolster consumer repurchase intentions, ultimately enhancing distribution channel effectiveness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2496-2512
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• 2021

The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption.