• Journal of IKEEE
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• v.22 no.3
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• pp.829-835
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• 2018

Recently, studies have been actively conducted to reduce spare power that is unnecessarily generated or wasted in existing power systems and to improve energy use efficiency. In this study, smart meter, which is one of the element technologies of smart grid, is implemented to improve the efficiency of energy use by controlling power of electric devices, and predicting trends of energy usage based on deep learning. We propose and develop an algorithm that controls the power of the electric devices by comparing the predicted power consumption with the real-time power consumption. To verify the performance of the proposed smart meter based on the deep running, we constructed the actual power consumption environment and obtained the power usage data in real time, and predicted the power consumption based on the deep learning model. We confirmed that the unnecessary power consumption can be reduced and the energy use efficiency increases through the proposed deep learning-based smart meter.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3029-3045
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• 2017

The main processes of a cognitive radio system include spectrum sensing, spectrum decision, spectrum sharing, and spectrum conversion. Experimental results show that these stages introduce a time delay that affects the spectrum sensing accuracy, reducing its efficiency. To reduce the time delay, the frequency spectrum prediction was proposed to alleviate the burden on the spectrum sensing. In this paper, the deep recurrent neural network (DRNN) was proposed to predict the spectrum of multiple time slots, since the existing methods only predict the spectrum of one time slot. The continuous state of a channel is divided into a many time slots, forming a time series of the channel state. Since there are more hidden layers in the DRNN than in the RNN, the DRNN has fading memory in its bottom layer as well as in the past input. In addition, the extended Kalman filter was used to train the DRNN, which overcomes the problem of slow convergence and the vanishing gradient of the gradient descent method. The spectrum prediction based on the DRNN was verified with a WiFi signal, and the error of the prediction was analyzed. The simulation results proved that the multiple slot spectrum prediction improved the spectrum efficiency and reduced the energy consumption of spectrum sensing.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.436-454
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• 2016

This research presents the battery discharge rate models for the energy consumption of mobile phone batteries based on machine learning by taking into account three usage patterns of the phone: the standby state, video playing, and web browsing. We present the experimental design methodology for collecting data, preprocessing, model construction, and parameter selections. The data is collected based on the HTC One X hardware platform. We considered various setting factors, such as Bluetooth, brightness, 3G, GPS, Wi-Fi, and Sync. The battery levels for each possible state vector were measured, and then we constructed the battery prediction model using different regression functions based on the collected data. The accuracy of the constructed models using the multi-layer perceptron (MLP) and the support vector machine (SVM) were compared using varying kernel functions. Various parameters for MLP and SVM were considered. The measurement of prediction efficiency was done by the mean absolute error (MAE) and the root mean squared error (RMSE). The experiments showed that the MLP with linear regression performs well overall, while the SVM with the polynomial kernel function based on the linear regression gives a low MAE and RMSE. As a result, we were able to demonstrate how to apply the derived model to predict the remaining battery charge.

• Journal of Microbiology and Biotechnology
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• v.14 no.6
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• pp.1163-1169
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• 2004

An unstructured mathematical model is presented for lactic acid fermentation based on the energy balance. The proposed model reflects the energy metabolic state and then predicts the cell growth, lactic acid production, and glucose consumption rates by relating the above rates with the energy metabolic rate. Fermentation experiments were conducted under various initial lactic acid concentrations of 0, 30, 50, 70, and 90 g/l. Also, a genetic algorithm was used for further optimization of the model parameters and included the operations of coding, initialization, hybridization, mutation, decoding, fitness calculation, selection, and reproduction exerted on individuals (or chromosomes) in a population. The simulation results showed a good fit between the model prediction and the experimental data. The genetic algorithm proved to be useful for model parameter optimization, suggesting wider applications in the field of biological engineering.

• Journal of the Korean Solar Energy Society
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• v.31 no.5
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• pp.27-32
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• 2011

Most of domestic building generally don't have fixed shading devices considering of appearance and aesthetic issues. In this study is suggested that tilt window simultaneously has a role of shading and blocking solar radiation. The tilt window thermal performance is investigated by relation ship between inclination and heating cooling road. As comparing vertical window with $5^{\circ}$ and $7^{\circ}$ of tilt window respectively, the heating load is increased by 3.6% and cooling load is reduced by 8.1% on $5^{\circ}$ tilt window and the heating load is increased by 5.3% and cooling load is reduced by 11.5% on $5^{\circ}$ tilt window. Especially, the total load of alternative tilt window is showed the reduction rate 2.6% and3.6% compared of vertical window. Therefore, the tilt window is possible to role of shading of solar radiation and reduction of heating and cooling load.

• Korean Journal of Construction Engineering and Management
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• v.23 no.4
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• pp.26-35
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• 2022

The 7th education reform in 1997 has led changes in the way buildings were constructed and such changes drove educational facilities to steadily consume more energy every year. Also, these facilities take several years' estimated expenditure as well as the increased unit price of electricity into account when planning their annual operating budget. Such circumstances may adversely affect the establishment of their budget plan since improper allocation of operating costs could take place. To propose educational facilities' operating budget management plan on electrical energy consumption, this study developed a model that help oversee the facilities' consumption of electrical energy. For the model development, the primary core variables related to electrical energy factors from the aspects of surroundings, physics, policy, etc. were derived from taking both literature research and the characteristics of these facilities into account. The secondary core variables were then derived using the correlation analysis. Lastly, the electric energy use prediction model was developed by performing regression analysis based on the derived secondary core variables.

• Computers and Concrete
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• v.14 no.3
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• pp.247-262
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• 2014

Partial replacement of Portland cement by slag can reduce the energy consumption and $CO_2$ emission therefore is beneficial to circular economy and sustainable development. Compressive strength is the most important engineering property of concrete. This paper presents a numerical procedure to predict the development of compressive strength of slag blended concrete. This numerical procedure starts with a kinetic hydration model for cement-slag blends by considering the production of calcium hydroxide in cement hydration and its consumption in slag reactions. Reaction degrees of cement slag are obtained as accompanied results from the hydration model. Gel-space ratio of hardening slag blended concrete is determined using reaction degrees of cement and slag, mixing proportions of concrete, and volume stoichiometries of cement hydration and slag reaction. Furthermore, the development of compressive strength is evaluated through Powers' gel-space ratio theory considering the contributions of cement hydration and slag reaction. The proposed model is verified through experimental data on concrete with different water-to-binder ratios and slag substitution ratios.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.1
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• pp.46-53
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• 2013

Recently, intensive research has been performed for reducing video decoder energy consumption, especially based on DVFS (Dynamic Voltage and Frequency Scaling) technique. Our previous work [1] has proposed the optimal DVFS algorithm for energy reduction in video decoders. In spite of the mathematical optimality of the algorithm, the precondition of known frame decoding cycle/complexity limits its application to some realistic scenarios. This paper overcomes this limitation by frame data size-based estimation of frame decoding complexity. The proposed decoding complexity estimation method shows over 90% accuracy. And with this estimation method and buffer underflow margin of around 20% of frame size, almost same power consumption reduction performance as the optimal algorithm can be achieved.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5321-5329
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• 2011

Space Layout has been an issue in the facet of reducing the co2 in that the transportation sector has been to represent almost more than 20% of the total energy consumption for decades. Beside the development of the more efficient transportation systems, an efficient space layout makes it possible to reduce the amount of energy consumption in the transportation sector through allocating the sub-spaces in such an arrangement of minimizing the travel distances. In line with this thinking, this research aims at implementing a simulator which can calculate the vehicle-based travel distance upon a certain space layout. Based on the findings that the vehicle-based travels take place between the two functionally related sub-spaces, this research addresses a method of calculating the vehicle-based travel distance by multiplying the traffic volume of each sub-spaces by the travel distance to the other connected sub-spaces.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.169-178
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• 2019

Now a day, we can perform various predictions by applying machine learning, which is a field of artificial intelligence; however, the finding of best algorithm in the field is always the problem. This paper predicts monthly power trading amount, monthly power trading amount of money, monthly index of production extension, final consumption of energy, and diesel for automotive using machine learning supervised algorithms. Then, we find most fit algorithm among them for each case. To do this we show the probability of predicting the value for monthly power trading amount and monthly power trading amount of money, monthly index of production extension, final consumption of energy, and diesel for automotive. Then, we try to average each predicting values. Finally, we confirm which algorithm is the most superior algorithm among them.