• Journal of the Korean Society for Precision Engineering
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• v.30 no.5
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• pp.461-466
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• 2013

In this paper, main mechanism and measurement method of energy consumption for machine tools are investigated by experiment and simulation. To evaluate total energy consumption of the machine tools, standard test workpiece and measuring method and test procedures are suggested. And, improvement of energy consumption evaluation by the motion kinematics theory is used. In addition, to estimate energy consumption of machine tools in design process, mass distribution of the structure and 5 axis motions are investigated and simulated by numerical analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.633-636
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• 2022

The reduction of energy consumption at the base station (BS) has become more important recently. In this paper, we consider the adaptive muting of the antennas based on the predicted future traffic load to reduce the energy consumption where the number of active antennas is adaptively adjusted according to the predicted future traffic load. Given that traffic load is sequential data, three different RNN structures, namely long-short term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) are considered for the future traffic load prediction. Through the performance evaluation based on the actual traffic load collected from the Afghanistan telecom company, we confirm that the traffic load can be estimated accurately and the overall power consumption can also be reduced significantly using the antenna musing.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.1
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• pp.23-29
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• 2015

Energy constraints of wireless sensor networks are an important challenge. Data Transmission requires energy. Distance between origin and destination has an important role in energy consumption. In addition, the location of base station has a large impact on energy consumption and a specific method not proposed for it. In addition, a obtain model for location of base station proposed. Also a model for distributed clustering is presented by cluster heads. Eventually, a combination of discussed ideas is proposed to improve the energy consumption. The proposed ideas have been implemented over the LEACH-C protocol. Evaluation results show that the proposed methods have a better performance in energy consumption and lifetime of the network in comparison with similar methods.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.1
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• pp.14-21
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• 2021

Green networking has become a issue to reduce energy wastes and CO2 emission by adding energy managing mechanism to wired data networks. Energy consumption of the overall wired data networks is driven by access networks, expect for end devices. However, on a global scale, it is more difficult to manage centrally energy, measure and model the real energy use and energy savings potential of the access networks. This paper presented the multiple linear regression model to predict energy consumption of wired access networks using supervised learning of machine learning with data collected by existing investigated materials, actual measured values and results of many models. In addition, this work optimized the performance of it by various experiments and predict energy consumption of wired access networks. The performance evaluation of the regression model was achieved by well-knowned evaluation metrics.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1256-1263
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• 2022

With the advent of the Fourth Industrial Revolution, the amount of energy used in buildings has been increasing due to changes in the energy use structure caused by the massive spread of information-oriented equipment, climate change and greenhouse gas emissions. For the efficient use of energy, it is necessary to have a plan that can predict and reduce the amount of energy use according to the type of energy source and the use of buildings. To address such issues, this study presents a model embedded in a digital twin that predicts energy use in buildings. The digital twin is a system that can support a solution of urban problems through the process of simulations and analyses based on the data collected via sensors in real-time. To develop the energy use prediction model, energy-related data such as actual room use, power use and gas use were collected. Factors that significantly affect energy use were identified through a correlation analysis and multiple regression analysis based on the collected data. The proof-of-concept prototype was developed with an exhibition facility for performance evaluation and validation. The test results confirm that the error rate of the energy consumption prediction model decreases, and the prediction performance improves as the data is accumulated by comparing the error rates of the model. The energy use prediction model thus predicts future energy use and supports formulating a systematic energy management plan in consideration of characteristics of building spaces such as the purpose and the occupancy time of each room. It is suggested to collect and analyze data from other facilities in the future to develop a general-purpose energy use prediction model.

• Advances in Energy Research
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• v.6 no.2
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• pp.91-101
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• 2019

This paper proposes an efficient data-driven approach to build models for predicting energy consumption in buildings. Data used in this research is collected by installing humidity and temperature sensors at different locations in a building. In addition to this, weather data from nearby weather station is also included in the dataset to study the impact of weather conditions on energy consumption. One of the main emphasize of this research is to make feature selection independent of domain knowledge. Therefore, to extract useful features from data, two different approaches are tested: one is feature selection through principal component analysis and second is relative importance-based feature selection in original domain. The regression model used in this research is gradient boosting regression and its optimal parameters are chosen through a two staged coarse-fine search approach. In order to evaluate the performance of model, different performance evaluation metrics like r2-score and root mean squared error are used. Results have shown that best performance is achieved, when relative importance-based feature selection is used with gradient boosting regressor. Results of proposed technique has also outperformed the results of support vector machines and neural network-based approaches tested on the same dataset.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.93-99
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• 2019

In this paper, we propose a smart meter that uses GRU model, which is one of artificial neural networks, for the efficient energy management. We collected power consumption data that train GRU model through the proposed smart meter. The implemented smart meter has automatic power measurement and real-time observation function and load control function through power consumption prediction. We determined a reference value to control the load by using Root Mean Squared Error (RMS), which is one of performance evaluation indexes, with 20% margin. We confirmed that the smart meter with automatic load control increases the efficiency of energy management.

• KIEAE Journal
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• v.17 no.3
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• pp.75-81
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• 2017

Purpose: The energy consumption of apartment units is affected by the lifestyle of the residents rather than system technology. In this study the numerical analysis of assumed energy consumption correlation factors with arbitrary value due to uncertainty. It is intended to be used as a simulation correction value which can be utilized as a predicted value of actual energy usage. The correction value of the simulation is set in the developed form of the existing process that derives the actual usage amount. The simulation results used in the existing evaluation system are used to maintain the useful value as the current system evaluation scale and predict the actual capacity. Method: The method of the study is to statistically analyze the data frames of all complexes capable of collecting the annual energy usage and to reconstruct the population by adding the variables that are expected to be correlated. Repeat the data frame configuration with variables that are assumed to be highly correlated with energy use levels. Determine whether there is correlation or not. The intensity of the external characteristics of the building equipment related to the energy consumption is presented as the quantitative value. Result: The correlation between electricity consumption and trading price since 2010 is analyzed as (Correlation coefficient 0.82). These results are higher than (Correlation coefficient 0.79), which is the correlation between residential area and trading price. This paper signifies the starting point of the methodology that broadens the field of view of verification of simulation feasibility limited to the prediction technique focused on the simulation tool and the element technology scope.The diversified phenomenon reproduction method develops the existing energy simulation method.It can be completed with a simulation methodology that can infer actual energy consumption.

• 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.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.883-888
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• 2023

Since data centers are places that provide IT services 24 hours a day, 365 days a year, data center power consumption is expected to increase to approximately 10% by 2030, and the introduction of high-density IT equipment will gradually increase. In order to ensure the stable operation of IT equipment, various types of research are required to conserve energy in cooling and improve energy management. This study proposes the following process for energy saving in data centers. We conducted CFD modeling of the data center, proposed an artificial intelligence-based thermal environment prediction model, compared actual measured data, the predicted model, and the CFD results, and finally evaluated the data center's thermal management performance. It can be seen that the predicted values of RCI, RTI, and PUE are also similar according to the normalization used in the normalization method. Therefore, it is judged that the algorithm proposed in this study can be applied and provided as a thermal environment prediction model applied to data centers.