• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.4991-4996
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• 2010

Clustering method in wireless sensor network is the technique that forms the cluster to aggregate the data and transmit them at the same time that they can use the energy efficiently. Even though cluster group model is based on clustering, it differs from previous method that reducing the total energy consumption by separating energy overload to cluster group head and cluster head. In this thesis, I calculate the optimal cluster group number and cluster number in this kind of cluster group model according to threshold of energy consumption model. By using that I can minimize the total energy consumption in sensor network and maximize the network lifetime. I also show that proposed cluster group model is better than previous clustering method at the point of network energy efficiency.

• Journal of Korea Planning Association
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• v.54 no.5
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• pp.129-138
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• 2019

This study aims to empirically examine the relationship between building energy consumption and building and local factors in Seoul. Building energy issue is an important topic for low carbon and eco-friendly city development. Building physical, socio-economic and environmental factors effect to increasing or decreasing energy consumption. However, there are different characteristic in each area, and this kind of variable has a hierarchical structure. The multi-level model was used to consider the hierarchical structure of the variables. In this study, a multi-level model was applied to confirm the difference between areas. Spatial area is Seoul, Korea and the temporal scope is August, summer season. As the result, in Model 1 (Null Model), ICC is 0.817. This shows that the energy consumption differs by 8.174% due to factors at the Dong level. Model 2 (Random Intercept Model) suggests that building's physical factors and Average age, Household size and Land price in Dong level have significant effects on Building energy consumption. In Model 3 (Random Coefficient Model), random effect variables have intercepts and slopes to vary across groups. This study provides a perspective for policy makers that the building energy reduction policies to be applied for buildings should be differently applied on area. Furthermore, not only physical factors but also socio-economic and environmental factors are important when making energy reduction policy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.514-537
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• 2020

Wireless sensor network (WSN) is a distributed network composed of many sensory nodes. It is precisely due to the clustering unevenness and cluster head election randomness that the energy consumption of WSN is excessive. Therefore, a many-objective optimization WSN energy balance model is proposed for the first time in the clustering stage of LEACH protocol. The four objective is considered that the cluster distance, the sink node distance, the overall energy consumption of the network and the network energy consumption balance to select the cluster head, which to better balance the energy consumption of the WSN network and extend the network lifetime. A many-objective optimization algorithm to optimize the model (LEACH-ABF) is designed, which combines adaptive balanced function strategy with penalty-based boundary selection intersection strategy to optimize the clustering method of LEACH. The experimental results show that LEACH-ABF can balance network energy consumption effectively and extend the network lifetime when compared with other algorithms.

• Journal of Distribution Science
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• v.18 no.1
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• pp.71-83
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• 2020

Purpose: Because the energy consumption, economic growth and logistics development are still the heated topics which have attracted many scholars' interests. Therefore, this paper attempts to analyze the effect of logistics development on the economic growth, explore the effect of the economic growth on energy consumption and to discuss the effect of the logistics development on energy intensity. Research design, data and methodology: Using the panel data over the period 2000-2017 of 156 countries and employing the country & year fixed effect model, system generalized method moments and random effect model, the empirical analyses of this propositions are performed. Results: The empirical findings present that the logistics development is positively related to the economic growth. The energy consumption in the t-1 period and economic growth are positively related to the current energy consumption. The logistics development is negatively related to the energy intensity. Meanwhile, the empirical findings also indicate that there is a great difference about these effects among the four sub-samples (low income 18 countries, low middle income 49 countries, upper middle income 44 countries, high income 49 countries). Conclusions: Based on the evidences in this paper provided, we can find that these variables can affect each other.

• Proceedings of the KSR Conference
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• 2002.05a
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• pp.431-436
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• 2002

Train driving should be satisfied to run fixed distance within given time, and it is desirable to minimize energy consumption. Minimizing energy consumption depends on the train operation modes by driver or automatic operation. In this article, an optimal operation to minimize energy consumption by changing modes of train operation by a driver is investigated. First, powering model, braking model and consumed energy calculation model are introduced by using Matlab software. The accuracy of the model established by simulation is compared with the real experimental data, which is obtained from an authorized institution. Second, several simulations under a variety of operations in the ideal track are executed, and then the optimal pattern of train driving is found.

• Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
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• v.12 no.6
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• pp.579-590
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• 2018

Anomaly detection on building energy consumption has been regarded as an effective tool to reduce energy saving on building operation and maintenance. However, it requires energy model and FDD expert for quantitative model approach or large amount of training data for qualitative/history data approach. Both method needs additional time and labors. This study propose a machine learning and data science approach to define faulty conditions on hourly building energy consumption with reducing data amount and input requirement. It suggests an application of Support Vector Data Description (SVDD) method on training normal condition of hourly building energy consumption incorporated with hourly outdoor air temperature and time integer in a week, 168 data points and identifying hourly abnormal condition in the next day. The result shows the developed model has a better performance when the ${\nu}$ (probability of error in the training set) is 0.05 and ${\gamma}$ (radius of hyper plane) 0.2. The model accuracy to identify anomaly operation ranges from 70% (10% increase anomaly) to 95% (20% decrease anomaly) for daily total (24 hours) and from 80% (10% decrease anomaly) to 10%(15% increase anomaly) for occupied hours, respectively.

• 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 Solar Energy Society
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• v.30 no.6
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• pp.89-94
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• 2010

A large portion of the energy cost of a building is cooling and heating to maintain a comfortable indoor environment. Air conditioning is now one of the important parts in the building design, as increase in energy consumption and pollutant emission in energy conversion process. In this study, elements that affects the energy consumption of model building are identified and the perfomance analysis of the alternative a Low Energy Cooling Systems considering characteristics of model building and energy saving performance is analyzed. In this study, elements that affect the energy consumption of office building are identified and energy saving performance of the alternative air conditioning system is analyzed. As a result, applied to earn and suggest basic data for energy saving measures. In this study, EnergyPlus simulation program was used to evaluate the energy load when alternative Low Energy Cooling Systems are applied to the model building. The reliability of simulation program is verified by comparing actual energy load from operation data of building management office and predicted energy load using simulation program. For Low Energy Cooling System application which considers the purpose and characteristics of the building, reasonable and energy-saving air conditioning method obtained by analyzing energy consumption elements for each expected air conditioning methods is used to deduct result of this study.

• Proceedings of the KSR Conference
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• 2005.11a
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• pp.119-124
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• 2005

Decision of operation performance mode to minimize the energy consumption of urban rail vehicle. This paper analyses how much acceleration and deceleration of urban rail vehicle should be applied andhow to choose an operation mode to minimize energy consumption when train runs between station within the fixed operation time. The decided operation pattern satisfying the minimum energy consumption becomes a target trajectory and a basis for the controller design criteria. To make this goal it grasps the characteristics of urban rail vehicle, realize operation energy model of urban rail vehicle and verify the accuracy of embodied model the Matlab simulation with the same operation result of real route. It searches for operation pattern to minimize operation energy by changing the acceleration and deceleration on the imaginative route and proposes operation pattern minimizing energy consumption by applying real operation data between Dolgogee-Sukgye section of Seoul Metropolitan Subway Line 6.

• 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.