• International Journal of Contents
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• v.15 no.4
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• pp.113-119
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• 2019

It has been reported that large amounts of information on agri-foods were delivered to consumers through television and social networks, and the information may influence consumers' behavior. The purpose of this paper was first to analyze relations of social network service and broadcasting program on paprika consumption in the aspect of amounts to purchase and identify potential factors that can promote paprika consumption; second, to develop prediction models of paprika consumption by using structured and unstructured big data. By using data 2010-2017, cross-correlation and time-series prediction algorithms (autoregressive exogenous model and vector error correction model), statistically significant correlations between paprika consumption and television programs/shows and blogs mentioning paprika and diet were identified with lagged times. When paprika and diet related data were added for prediction, these data improved the model predictability. This is the first report to predict paprika consumption by using structured and unstructured data.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.28 no.2
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• pp.29-35
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• 2020

In this paper, we established a prediction model of fuel consumption at the aircraft's taxi operation. To look for countermeasures to reduce fuel consumption and carbon emissions, Airbus A380's actual ground taxi data was used. As a result, the number of stops or turnings during the taxi operation was not related to fuel consumption. It was confirmed that the amount of fuel consumption in the taxi operation was the taxi time and the thrust change. It can be confirmed that ground control optimization, which is the result of close cooperation between the control organization and the airline, is absolutely necessary to reduce taxi time and minimize the occurrence of thrust change events.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.175-181
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• 2021

This paper analyzes the artificial intelligence-based approach for short-term energy consumption prediction. In this paper, we employ the reinforcement learning algorithms to improve the limitation of the supervised learning algorithms which usually utilize to the short-term energy consumption prediction technologies. The supervised learning algorithm-based approaches have high complexity because the approaches require contextual information as well as energy consumption data for sufficient performance. We propose a deep reinforcement learning algorithm based on multi-agent to predict energy consumption only with energy consumption data for improving the complexity of data and learning models. The proposed scheme is simulated using public energy consumption data and confirmed the performance. The proposed scheme can predict a similar value to the actual value except for the outlier data.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.10 no.2
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• pp.74-79
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• 2011

The goal of this research is to reduce dynamic and static power consumption for a low power cache system. The proposed cache can achieve a low power consumption by using a drowsy and a way prediction mechanism. For reducing the static power, the drowsy technique is used at 4-way set associative cache. And for reducing the dynamic energy, one among four ways is selectively accessed on the basis of information in the Way-Line Prediction Unit (WLPU). This prediction mechanism does not introduce any additional delay though prediction misses are occurred. The WLPU can effectively reduce the performance overhead of the conventional drowsy caching by waking only a drowsy cache line and one way in advance. Our results show that the proposed cache can reduce the power consumption by about 40% compared with the 4-way drowsy cache.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.64-72
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• 2016

In this paper, we utilize a Gaussian process to predict the power consumption in the air-conditioning system. As the power consumption in the air-conditioning system takes a form of a time-series and the prediction of the power consumption becomes very important from the perspective of the efficient energy management, it is worth to investigate the time-series model for the prediction of the power consumption. To this end, we apply the Gaussian process to predict the power consumption, in which the Gaussian process provides a prior probability to every possible function and higher probabilities are given to functions that are more likely consistent with the empirical data. We also discuss how to estimate the hyper-parameters, which are parameters in the covariance function of the Gaussian process model. We estimated the hyper-parameters with two different methods (marginal likelihood and leave-one-out cross validation) and obtained a model that pertinently describes the data and the results are more or less independent of the estimation method of hyper-parameters. We validated the prediction results by the error analysis of the mean relative error and the mean absolute error. The mean relative error analysis showed that about 3.4% of the predicted value came from the error, and the mean absolute error analysis confirmed that the error in within the standard deviation of the predicted value. We also adopt the non-parametric Wilcoxon's sign-rank test to assess the fitness of the proposed model and found that the null hypothesis of uniformity was accepted under the significance level of 5%. These results can be applied to a more elaborate control of the power consumption in the air-conditioning system.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.301-307
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• 2022

Energy consumption has grown alongside dramatic population increases. Statistics show that buildings in particular utilize a significant amount of energy, worldwide. Because of this, building energy prediction is crucial to best optimize utilities' energy plans and also create a predictive model for consumers. To improve energy prediction performance, this paper proposes a ResNet-LSTM model that combines residual networks (ResNets) and long short-term memory (LSTM) for energy consumption prediction. ResNets are utilized to extract complex and rich features, while LSTM has the ability to learn temporal correlation; the dense layer is used as a regression to forecast energy consumption. To make our model more robust, we employed Huber loss during the optimization process. Huber loss obtains high efficiency by handling minor errors quadratically. It also takes the absolute error for large errors to increase robustness. This makes our model less sensitive to outlier data. Our proposed system was trained on historical data to forecast energy consumption for different time series. To evaluate our proposed model, we compared our model's performance with several popular machine learning and deep learning methods such as linear regression, neural networks, decision tree, and convolutional neural networks, etc. The results show that our proposed model predicted energy consumption most accurately.

• 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.

• 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.

• 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 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.