• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.675-683
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• 2015

In the 21st century, we now face the serious problems of the enormous consumption of the energy resources. Depending on the power consumption increases, both China and South Korea face a reduction in available resources. This paper considers the regression models and time-series models to compare the performance of the forecasting accuracy based on Mean Absolute Percentage Error (MAPE) in order to forecast the electricity demand accurately on the short-term period (68 months) data in Northeast China and find the relationship with Korea. Among the models the support vector regression (SVR) model shows superior performance than time-series models for the short-term period data and the time-series models show similar results with the SVR model when we use long-term period data.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.1
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• pp.70-77
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• 2011

Sewage treatment plants, consuming 1,756[GWh] which is 0.53[%] of national wide electricity consumption, is one of the electricity consuming facilites. At the research of electricity consumption and power quality analysis on sewage treatment plants, average utilization of transformer was less than 40[%] because peak load was very lower than its capacity due to excess capacity. So reduction of power loss can be achieved by transformer design optimization. The achievement in this research, is to meet reduction of power loss through optimizing the capacity and to improve as high efficiency-low loss transformer while the transformer is operating.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.11 no.2
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• pp.244-249
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• 1999

This study is to examine the emission rate of $CO_2$gas as the environmental load in office buildings. After the investigation of monthly consumption of each energy source(electricity and natural gas), it is analyzed that the $CO_2$emission rate of 34 office buildings surveyed is 22.4kg-$c/m^2$.year, which consists of 17.5kg-$c/m^2$.year by consuming electricity, and 4.9kg-$c/m^2$.year by consuming natural gas. And the $CO_2$emission rate of each load in those buildings consists of 68% emitted by general electricity, 16% by cooling load and 16% by heating load. It is also proposed that the $CO_2$emission rate of cooling and heating load is profoundly pertinent to the variation of outdoor temperature.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.21-31
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• 2020

This study investigates the dynamic effects of economic development, international cooperation, electricity consumption, and political risk on the escalation of CO₂ emission in Vietnam. We adopted autoregressive distributed lag model and Granger causality method to examine the interaction between CO₂ and various economic and political factors, including foreign direct investment, trade openness, economic growth, manufacture, electricity consumption, and political risk in Vietnam since the economic revolution in 1986. The findings reflect opposite influence between these factors and the level of CO₂ in the intermediate and long-term durations. Accordingly, foreign direct investment and CO₂ emission have a bidirectional relationship, in which foreign direct investment accelerates short-term CO₂ emission, but reduces it in the long run through an interactive mechanism. Moreover, economic development increases the volume of CO₂ emission in both short and long run. There was also evidence that political risk has a negative effect on the environment. Overall, the findings confirm lasting negative environmental effects of economic growth, trade liberalization, and increased electricity consumption. These factors, with Granger causality, mutually affect the escalation of CO₂ in Vietnam. In order to control the level of CO₂, more efforts are required to improve administrative transparency, attract high-quality foreign investment, and decouple the environment from economic development.

• Journal of Energy Engineering
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• v.7 no.2
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• pp.223-230
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• 1998

Energy consumption statistics have been surveyed for 50 large commercial buildings with high energy consumption records in Seoul City. The buildings were classified into three different groups for data analysis: hospitals, hotels, and department stores/office buildings. The analysis was focused on identifying installed boiler and refrigerator data, energy consumption rates, and energy load distribution throughout the year. Refrigerating electricity was confirmed again to affect most on the formation of the summer electricity load peak as expected. Replacing the refrigerator electricity peak in the summer with co-generation in large commercial buildings. However, overall heat load distribution in a single building is still considered not large enough for economically feasible co-generation and thus joint co-generation for multiple neighboring buildings are preferred and the Electric Power Law and LNG pricing policy should be revised favorably for co-generation in advance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1404-1423
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• 2015

In smart grid, electricity consumption data may be handed over to a third party for various purposes. While government regulations and industry compliance prevent utility companies from improper or illegal sharing of their customers' electricity consumption data, there are some scenarios where it can be very useful. For example, it allows the consumers' data to be shared among various energy resources so the energy resources are able to analyze the data and adjust their operation to the actual power demand. However, it is crucial to protect sensitive electricity consumption data during the sharing process. In this paper, we propose a fine-grained access control scheme (FAC) with efficient attribute revocation and policy updating in smart grid. Specifically, by introducing the concept of Third-party Auditor (TPA), the proposed FAC achieves efficient attribute revocation. Also, we design an efficient policy updating algorithm by outsourcing the computational task to a cloud server. Moreover, we give security analysis and conduct experiments to demonstrate that the FAC is both secure and efficient compared with existing ABE-based approaches.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.149-160
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• 2019

The electricity consumption time series data of 'A' University from July 2016 to June 2017 is analyzed via nonparametric functional data clustering since the time series data can be regarded as realization of continuous functions with dependency structure. We use a Bouveyron and Jacques (Advances in Data Analysis and Classification, 5, 4, 281-300, 2011) method based on model-based functional clustering with an FEM algorithm that assumes a Gaussian distribution on functional principal components. Clusterwise analysis is provided with cluster mean functions, densities and cluster profiles.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.10-16
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• 2023

The ever-increasing amount of data generated by various industries and systems has led to the development of data mining techniques as a means to extract valuable insights and knowledge from such data. The electrical energy industry is no exception, with the large amounts of data generated by SCADA systems. This study focuses on the analysis of historical data recorded in the SCADA database of the Libyan Electricity Company. The database, spanned from January 1st, 2013, to December 31st, 2022, contains records of daily date and hour, energy production, temperature, humidity, wind speed, and energy consumption levels. The data was pre-processed and analyzed using the WEKA tool and the Apriori algorithm, a supervised machine learning technique. The aim of the study was to extract association rules that would assist decision-makers in making informed decisions with greater efficiency and reduced costs. The results obtained from the study were evaluated in terms of accuracy and production time, and the conclusion of the study shows that the results are promising and encouraging for future use in the Libyan Electricity Company. The study highlights the importance of data mining and the benefits of utilizing machine learning technology in decision-making processes.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.3
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• pp.391-395
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• 2016

In the last few decades, global electricity consumption has dramatically increased and has become drastically fluctuating and uncertain causing blackout. Due to the unexpected peak electricity demand, we need significant electricity supply. The solutions to these problems are smart grid system which is envisioned as future power system. Smart grid system can reduce electricity peak demand and induce effective electricity consumption through various price policies, demand response (DR) control methodologies, and state-of-the-art smart equipments in order to optimize electricity resource usage in an intelligent fashion. Demand response (DR) is one of the key technologies to enable smart grid. In this paper, we propose greedy technique for demand response smart grid system. The proposed scheme focuses on minimizing electricity bills, preventing system blackout and sacrificing user convenience.

• Environmental and Resource Economics Review
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• v.22 no.2
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• pp.251-279
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• 2013

Prices of oil products such as gasoline and diesel are deregulated since 1997 while electricity price is still controlled by government. This difference may explain recent discrepancy in the patterns of demand for oil products and electricity - constant increase in electricity consumption and stagnant demand for oil. To verify it empirically, we estimate price and income (production) elasticity of demand across time by using a rolling regression with 10 year-window based on monthly data for 1981-2011. Estimation results show that the sensitivity to price in demand for gasoline and diesel has increased since mid-90s while the elasticity of demand for electricity has become smaller. Second, income (production) elasticities of demand have shown no significant changes for both oil products and electricity. Third, cross-price elasticity was found meaningful only for gasoline before mid 1990s and for diesel after then.