• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.14 no.1
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• pp.16-21
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• 2018

This work aims to develop a platform to investigate the effect of operation schedules on the building energy consumption and to derive a simulation model based optimal start and stop daily strategy. An open-source building energy simulation tool DOE2 is used for the engine, and the developed simulation model is validated using ASHRAE guideline 14. The effect of late-start/early-stop operation of HVAC system on the daily building energy consumption was analyzed using the developed simulation model. It was found that about 10% of energy consumption cut was possible using the control strategy for an hour of advance of the stop operation, and about 3% per an hour of delay of the start operation.

• Journal of the Korean Society of Food Culture
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• v.22 no.4
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• pp.414-419
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• 2007

The purpose of this study was to measure the effect of disgust with meat mediating the factors influencing meat consumption. Structural equation model was used to measure the causal relationships among constructs. The structural analysis Result of the data indicated excellent model fit. The effects of moral concerns for animals, meat texture and satiety from meat on disgust with meat were statistically significant. The effects of color in meat and negative body esteem on disgust with meat were not statistically significant. As expected, disgust with meat had a significant effect on meat consumption. Moreover, disgust with meat played a mediating role in the relationship between moral concerns for animals and meat consumption. Disgust with meat played a mediating role in the relationship between satiety from meat and meat consumption. Disgust with meat did not play a mediating role in the relationship between color in meat and meat consumption. Disgust with meat did not play a mediating role in the relationship between body esteem and meat consumption. In conclusion, based on structural analysis, a model was proposed of interrelations among constructs. It should be noted that the original model was modified and should, preferably, be validated in future research.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.589-602
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• 2020

The development of smart grids has enabled the easy collection of a large amount of power data. There are some common patterns that make it useful to cluster power consumption patterns when analyzing s power big data. In this paper, clustering analysis is based on distance functions for time series and clustering algorithms to discover patterns for power consumption data. In clustering, we use 10 distance measures to find the clusters that consider the characteristics of time series data. A simulation study is done to compare the distance measures for clustering. Cluster validity measures are also calculated and compared such as error rate, similarity index, Dunn index and silhouette values. Real power consumption data are used for clustering, with five distance measures whose performances are better than others in the simulation.

• International journal of advanced smart convergence
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• v.4 no.1
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• pp.45-53
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• 2015

This paper is proposed mathematical load modelling based on system identification approach of energy consumption of residential air conditioning. Due to air conditioning is one of the significant equipment which consumes high energy and cause the peak load of power system especially in the summer time. The demand response is one of the solutions to decrease the load consumption and cutting peak load to avoid the reservation of power supply from power plant. In order to operate this solution, mathematical modelling of air conditioning which explains the behaviour is essential tool. The four type of linear model is selected for explanation the behaviour of this system. In order to obtain model, the experimental setup are performed by collecting input and output data every minute of 9,385 BTU/h air-conditioning split type with $25^{\circ}C$ thermostat setting of one sample house. The input data are composed of solar radiation ($W/m^2$) and ambient temperature ($^{\circ}C$). The output data are power and energy consumption of air conditioning. Both data are divided into two groups follow as training data and validation data for getting the exact model. The model is also verified with the other similar type of air condition by feed solar radiation and ambient temperature input data and compare the output energy consumption data. The best model in term of accuracy and model order is output error model with 70.78% accuracy and $17^{th}$ order. The model order reduction technique is used to reduce order of model to seven order for less complexity, then Kalman filtering technique is applied for remove white Gaussian noise for improve accuracy of model to be 72.66%. The obtained model can be also used for electrical load forecasting and designs the optimal size of renewable energy such photovoltaic system for supply the air conditioning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2676-2689
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• 2013

Due to increased consumption of energy in the building environment, the building energy management systems (BEMS) solution has been developed to achieve energy saving and efficiency. However, because of the shortage of building energy management specialists and incompatibility among the energy management systems of different vendors, the BEMS solution can only be applied to limited buildings individually. To solve these problems, we propose a building cluster based remote energy monitoring and management (EMM) system and its functionalities and roles of each sub-system to simultaneously manage the energy problems of several buildings. We also introduce a novel energy demand forecasting algorithm by using past energy consumption data. Extensive performance evaluation study shows that the proposed regression based energy demand forecasting model is well fitted to the actual energy consumption model, and it also outperforms the artificial neural network (ANN) based forecasting model.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.1-6
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• 2024

Energy efficiency in wireless sensor networks (WSNs) is a critical issue because batteries are used for operation and communication. In terms of scalability, energy efficiency, data integration, and resilience, WSN-cluster-based routing algorithms often outperform routing algorithms without clustering. Low-energy adaptive clustering hierarchy (LEACH) is a cluster-based routing protocol with a high transmission efficiency to the base station. In this paper, we propose an energy consumption model for LEACH and compare it with the existing LEACH, advanced LEACH (ALEACH), and power-efficient gathering in sensor information systems (PEGASIS) algorithms in terms of network lifetime. The energy consumption model comprises energy-sensitive cluster formation and a cluster head selection technique. The setup and steady-state phases of the proposed model are discussed based on the cluster head selection. The simulation results demonstrated that a low-energy-consumption network was introduced, modeled, and validated for LEACH.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2302-2324
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• 2014

Cluster-based wireless sensor network (WSN) can significantly reduce the energy consumption by data aggregation and has been widely used in WSN applications. However, due to the intrinsic many-to-one traffic pattern in WSN, the network lifetime is generally deteriorated by the unbalanced energy consumption in a cluster-based WSN. Therefore, energy efficiency and network lifetime improvement are two crucial and challenging issues in cluster-based WSNs. In this paper, we propose a Non-Uniform Node Distribution (NUND) scheme to improve the energy efficiency and network lifetime in cluster-based WSNs. Specifically, we first propose an analytic model to analyze the energy consumption and the network lifetime of the cluster-based WSNs. Based on the analysis results, we propose a node distribution algorithm to maximize the network lifetime with a fixed number of sensor nodes in cluster-based WSNs. Extensive simulations demonstrate that the theoretical analysis results determined by the proposed analytic model are consistent with the simulation results, and the NUND can significantly improve the energy efficiency and network lifetime.

• Journal of Korean Society of Transportation
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• v.29 no.4
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• pp.65-71
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• 2011

It is practically hard to measure vehicle fuel consumption required to evaluate the energy-related governmental policies and traffic management strategies; the existing methods are too simplified due to the limited field data available. Existing methods are even unable to reflect the amount of fuel consumed when vehicles accelerate and decelerate, and such technical limitations have reduced the quality of the policy evaluation. This study proposes a new fuel consumption model that simultaneously considers the effects of both cruising speed and acceleration/deceleration of vehicles. A new fuel consumption model was developed based on the simulation data generated by AVL Cruise, a vehicle simulation program. The estimated by the proposed model was compared against the one from the existing method. Comparison results showed that the proposed model provided much reliable estimate (fuel consumption) than the other did.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1133-1151
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• 2010

Energy balanced consumption routing is based on assumption that the nodes consume energy both in transmitting and receiving. Lopsided energy consumption is an intrinsic problem in low-cost sensor networks characterized by multihop routing and in many traffic overhead pattern networks, and this irregular energy dissipation can significantly reduce network lifetime. In this paper, we study the problem of maximizing network lifetime through balancing energy consumption for uniformly deployed low-cost sensor networks. We formulate the energy consumption balancing problem as an optimal balancing data transmitting problem by combining the ideas of corona cluster based network division and optimized transmitting state routing strategy together with data transmission. We propose a localized cluster based routing scheme that guarantees balanced energy consumption among clusters within each corona. We develop a new energy cluster based routing protocol called "OECR". We design an offline centralized algorithm with time complexity O (log n) (n is the number of clusters) to solve the transmitting data distribution problem aimed at energy balancing consumption among nodes in different cluster. An approach for computing the optimal number of clusters to maximize the network lifetime is also presented. Based on the mathematical model, an optimized energy cluster routing (OECR) is designed and the solution for extending OEDR to low-cost sensor networks is also presented. Simulation results demonstrate that the proposed routing scheme significantly outperforms conventional energy routing schemes in terms of network lifetime.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.6799-6806
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• 2015

Electric energy consumption is always followed by the introduction of diversity scale-up and state-of-the-art equipments in logistic centers. In order to analyze the status and the characteristic of the electric energy consumption quantitatively, and also to evaluate the efficiency of the electric energy, this research aims to develop an estimation model of standard electric energy consumption for logistic centers. The proposed model applies the thermodynamics theory so as to effectively reflect the peculiarity that the temperature in the logistic center influences the electric energy consumption. And the model consists of the energy consumed by the refrigerator, which can be subdivided into the heat conducted through the wall, the heat convected by the open doors and the heat lost into the goods, and the electric consumption of the machinery equipments. The model also includes a variety of explanatory variables to support an operator of logistics centers in evaluating the efficiency of energy consumption and establishing improvement strategies for energy efficiency. Application of the model developed in this study is discussed with observed data on energy consumption of a logistics center.