• Journal of Korean Society for Atmospheric Environment
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• v.31 no.4
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• pp.356-367
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• 2015

A consumption based study on the carbon dioxide ($CO_2$) emission change due to the changes in the bovine meat consumption behavior in Korea was carried out. It was found that if the consumption of bovine meat be reduced by half, the reduction amount of $CO_2$ emissions be over 0.8 $MtCO_2e$ in all senarios in 2023. This amount is equivalent to over 50% of the greenhouse gases (GHGs) emission reduction target in agriculture and forestry, and fishery, a significant reduction. It was also found that the $CO_2$ emission reduction amount in consumption-based approach was the largest when the consumption of the imported bovine meat be reduced, though the difference was not that large.

• 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.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.338-345
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• 2011

Nowadays mobile devices are used for various applications such as making voice/video calls, browsing the Internet, listening to music etc. The average battery consumption of each of these activities and the length of time a user spends on each one determines the battery lifetime of a mobile device. Previous methods have provided predictions of battery lifetime using a static battery consumption rate that does not consider user characteristics. This paper proposes an approach to predict a mobile device's available battery lifetime based on usage patterns. Because every user has a different pattern of voice calls, data communication, and video call usage, we can use such usage patterns for personalized prediction of battery lifetime. Firstly, we define one or more states that affect battery consumption. Then, we record time-series log data related to battery consumption and the use time of each state. We calculate the average battery consumption rate for each state and determine the usage pattern based on the time-series data. Finally, we predict the available battery time based on the average battery consumption rate for each state and the usage pattern. We also present the experimental trials used to validate our approach in the real world.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1237-1255
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• 2014

In wireless sensor networks (WSNs), hierarchical clustering is an efficient approach for lower energy consumption and extended network lifetime. In cluster-based multi-hop communications, a cluster head (CH) closer to the sink is loaded heavier than those CHs farther away from the sink. In order to balance the energy consumption among CHs, we development a novel cluster-based routing protocol for corona-structured wireless sensor networks. Based on the relaying traffic of each CH conveys, adequate radius for each corona can be determined through nearly balanced energy depletion analysis, which leads to balanced energy consumption among CHs. Simulation results demonstrate that our clustering approach effectively improves the network lifetime, residual energy and reduces the number of CH rotations in comparison with the MLCRA protocols.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.613-624
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• 2021

This paper analyzes the relationship between income distribution and energy consumption from a Pareto optimal approach. For this purpose, the causality relationship between electricity consumption per capita (kWh) with respect to country groups and energy consumption per capita (kg of oil equivalent) along with gross domestic product per capita was analyzed. In addition to this purpose, a Pareto analysis was conducted to determine the countries with the highest per capita national income, how much of the world total energy they consume, and whether the law of power in the energy and electricity markets exists. Finally, the impact of official development assistance provided to low-income countries by high-income countries on the low-income countries' electricity and energy consumption was analyzed. In other words, it was questioned whether pareto redistribution policies serve the purpose or not. The Engle-Granger causality approach was used in the analysis of the causality relationship between variables. Our analysis indicated that, first, the energy data of the country groups may be inadequate in revealing income inequalities. Second, the existence of Pareto law of power and global income inequality can be explained based on energy data. Finally, Pareto optimal redistribution policies to eliminate income inequality remain inadequate in practice.

• 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 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.

• IEMEK Journal of Embedded Systems and Applications
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• v.2 no.3
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• pp.164-173
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• 2007

In this paper, we present a time-triggered mechanism for providing energy consumption profiles in the level of C functions. The similar mechanisms have already been introduced at the previous researches such as PowerScope and ePRO. Instead, we, in this paper, introduce our efforts to extend these researches to incorporate power domains and DVS(Dynamic Voltage Scaling), then to provide GUI-based tool as a plug-in to ESTO which is an IDE for Embedded S/W development based on Eclipse. From our experimental results, we could conclude that our approach worked and produced consistent energy consumption profiles on the DVS-applied program codes, and also displayed function level and time domain power consumption information with diverse presentation skills such as tables, phi-chart, bar-chart, 2-D graphs, consequently, is expected to provide more ease-to-use and productive IDE for lower power embedded S/W developers.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.87-92
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• 2019

This paper describes the pattern analysis for data of the factory energy consumption by using machine learning method. While usual statistical methods or approaches require specific equations to represent the physical characteristics of the plant, machine learning based approach uses historical data and calculate the result effectively. Although rule-based approach calculates energy usage with the physical equations, it is hard to identify the exact equations that represent the factory's characteristics and hidden variables affecting the results. Whereas the machine learning approach is relatively useful to find the relations quickly between the data. The factory has several components directly affecting to the electricity consumption which are machines, light, computers and indoor systems like HVAC (heating, ventilation and air conditioning). The energy loads from those components are generated in real-time and these data can be shown in time-series. The various sensors were installed in the factory to construct the database by collecting the energy usage data from the components. After preliminary statistical analysis for data mining, time-series clustering techniques are applied to extract the energy load pattern. This research can attributes to develop Factory Energy Management System (FEMS).

• The Journal of Industrial Distribution & Business
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• v.10 no.5
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• pp.37-47
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• 2019

Purpose - It is well known that chemyon, referred to by Westerners as face, naturally penetrates the daily life of Asians and influences their cognition, emotion, and behavior. Studies related to chemyon have been conducted in marketing and consumer behavior fields (e.g., luxury products or brands, service failure and recovery, brand preferences, consumer decision making, wedding ceremony, gift giving). A bulk of studies demonstrate that chemyon influences consumption behavior in Asian consumers. Although chemyon significantly influences consumption behavior of Asian consumers, it is also a cultural phenomenon that is not completely explained within the Western viewpoint. Whereas a number of researchers have approached cross-cultural studies of Asian and Western consumers, a limited number of studies have examined it from the perspective of chemyom. The purpose of this study is to compare the phenomenon that chemyon (face) not only affects the consumption behavior of Asia and the West universally (pan-culturally), but also distinctively (culture-specifically). That is, the purpose of this study is to describe that chemyon (face) is not only a culture-specific phenomenon but also a universal phenomenon in the consumption behavior of Asian and Western consumers, even though the extent that chemyon (face) impacts consumption behavior is differentiated. This study aims to understand commonalities and differences between Asian and Western consumption behavior in terms of chemyon (face), and to suggest how to enhance marketing effectiveness in a global market based on understanding the consumption behavior of Asia and the West. Research design, data, and methodology - Using systematic literature review and meta-analysis, this study investigates consumption behavior of Asian and Western consumers from the perspective of chemyon (face). Systematic literature review was used to compare face (chemyon) consumption of Western consumers with that of Asian consumers. To verify systematic literature review, meta-analysis was also accomplished. Results - First, the influence of face (chemyon) on consumption behavior is observed in Western consumers as well as Asian consumers. Second, Asian consumers are more influenced by face (chemyon) than Western consumers. Conclusions - Overall, chemyon (face) can affect the consumption behavior of Asians as well as the consumption behavior of Westerners.