• Journal of The Institute of Information and Telecommunication Facilities Engineering
• /
• v.9 no.3
• /
• pp.107-111
• /
• 2010

This paper is concerned with data center remote management service for demanding forecasting and reduction of energy usage. More particularly, intelligent server rack, mounted on inside of the data center, collects information about energy usage and temperature per server. Using this information, management platform forecasts energy demand in the future and automatically makes report according green environment raw. By providing the remote management service through remote terminals, users are not tied to a time and place to control device inside the data center. In this way, the data center remote management service enhances operability of the facility.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
• /
• v.14 no.1
• /
• pp.8-15
• /
• 2018

The purpose of this study was to analyze the energy consumption according to the HVAC systems in 122 elementary schools. To do this, we classified schools according to the HVAC systems. Selected schools were classified to the following groups by the HVAC equipments: EHP, EHP and GHP together and GHP. In addition we divided schools by the number of classes. The main results are as follow: 1) Annual average energy consumption at schools was about 300~900(kWh/students), $30{\sim}50(kWh/m^2)$, 9,000~29,000((kWh/class) 2) The smaller schools, the higher the energy consumption per class; energy usage of 10~19 classes's schools were approximately 3 times higher than 40 classes's schools. 3) Schools where the EHP was installed had the lowest energy costs and energy usage. The difference in energy costs was lower than the difference in energy usage because of the fuel price and the ratio of energy sources.

• Proceedings of the SAREK Conference
• /
• 2009.06a
• /
• pp.1343-1347
• /
• 2009

In order to comply with the global energy crisis and environment problem, it is necessary to research and develop the energy utilization technology with the reduction of the primary energy usage. Although the increasing rate of energy consumption started to attenuate, the entire consumption of energy as well as $CO_2$ emission rate tends to increase steadily along with an economic growth in Korea. The energy demand in Korea increases by annual 3.7% during the period from 2000 to 2006. And it is expected that we should take a charge of the greenhouse gas reduction obligation by the Climatic Change Convention(Kyoto Protocol) during the 2nd pledge period($2013{\sim}2017$). According to the IEA report in 2005, the emission amount of carbon dioxide is the 10th place in the world, and the increasing rate is 4.7% annually. Considering the economic scale of Korea, the degradation of energy usage is inevitable when the greenhouse gas reduction obligation come into effect. Therefore, effective energy usage is a very important issue to minimize baneful influence on industrial and economic activities.

• Smart Media Journal
• /
• v.13 no.1
• /
• pp.18-23
• /
• 2024

Power efficiency management in smart farms is important due to its link to climate change. As climate change negatively impacts agriculture, future agriculture is expected to utilize smart farms to minimize climate impacts, but smart farms' power consumption may exacerbate the climate crisis due to the current electricity production system. Therefore, it is essential to efficiently manage and optimize the power usage of smart farms. In this study, we propose a system that monitors the power usage of smart farm equipment in real time and predicts the power usage one hour later using GRU. CT sensors are installed to collect power usage data, which are analyzed to detect and prevent abnormal patterns, and combined with IoT technology to efficiently manage and monitor the overall power usage. This helps to optimize power usage, improve energy efficiency, and reduce carbon emissions. The system is expected to improve not only the energy management of smart farms, but also the overall efficiency of energy use.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.8
• /
• pp.2490-2506
• /
• 2022

In Mobile Edge Computing (MEC), attackers can speculate and mine sensitive user information by eavesdropping wireless channel status and offloading usage pattern, leading to user privacy leakage. To solve this problem, this paper proposes a Privacy-preserving and Energy-efficient Offloading Algorithm (PEOA) based on Lyapunov optimization. In this method, a continuous Markov process offloading model with a buffer queue strategy is built first. Then the amount of privacy of offloading usage pattern in wireless channel is defined. Finally, by introducing the Lyapunov optimization, the problem of minimum average energy consumption in continuous state transition process with privacy constraints in the infinite time domain is transformed into the minimum value problem of each timeslot, which reduces the complexity of algorithms and helps obtain the optimal solution while maintaining low energy consumption. The experimental results show that, compared with other methods, PEOA can maintain the amount of privacy accumulation in the system near zero, while sustaining low average energy consumption costs. This makes it difficult for attackers to infer sensitive user information through offloading usage patterns, thus effectively protecting user privacy and safety.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.64 no.2
• /
• pp.85-91
• /
• 2015

With the increasing popularity of Smart Grid infrastructure, it is much easier to collect energy usage data using AMI (Advanced Measuring Instrument) from residential housing, buildings and factories. Several researches have been done to improve an energy efficiency by analyzing the collected energy usage data. However, it is not easy to store and analyze the energy data using a traditional relational database management system since the data size grows exponentially with an increasing popularity of Smart grid infrastructure. In this paper, we are proposing a Hadoop based Big data system to store and analyze energy usage data. Based on our limited experiments, Hadoop based energy data analysis is three times faster than that of a relational database management system based approach with the current system.

• Transactions of the Korean hydrogen and new energy society
• /
• v.23 no.5
• /
• pp.551-558
• /
• 2012

Due to high oil prices and global warming problems, researching an alternative energy source and decreasing the energy usage will be the key in the future. Unlike other alternative energy sources, geothermal energy is less dependent on the surrounding environment. Geothermal energy is the ideal energy source for buildings due to the simple and space saving installation. The system is semi permanent once it is installed and this will help reduce the energy usage in controlling the climate in buildings. Geothermal energy does not emit carbon dioxide and other gases that are harmful to the environment. Therefore geothermal energy will be the key in solving high oil prices and a decrease in fossil fuels by applying the geothermal energy system to homes to counter future energy crisis.

• Journal of Climate Change Research
• /
• v.3 no.3
• /
• pp.183-193
• /
• 2012

In this study, the S University's energy usage, greenhouse gas emissions situation and potential reduction amount were analyzed using a long-term energy analysis model, LEAP. In accordance with the VISION 2020 and university's own improvement plans, S University plans to complete a second campus through expansion constructions by 2020 and by allocating the needed land. Accordingly, increases in energy usage and greenhouse gas emissions seem inevitable. Hence, in this study, the calculations of potential reduction amount by 2020 were attempted through the use of LEAP model by categorizing the energy used based on usage types and by proposing usage typebased reduction methods. There were a total of 4 scenarios: a standard scenario that predicted the energy usage without any additional energy reduction activity; energy reduction scenario using LED light replacement; energy reduction scenario using high efficiency building equipment; and a scenario that combines these two energy reduction scenarios. As scenario-based results, it was ascertained that, through the scenario that had two other energy reduction scenarios combined, the 2020 greenhouse gas emissions amount would be 14,916 tons of $CO_2eq$, an increase of 43.7% compared to the 2010 greenhouse gas emissions amount. Put differently, it was possible to derive a result of about 23.7% reduction of the greenhouse gas emissions amount for S University's greenhouse gas emissions amount through energy reduction activities. In terms of energy reduction methods, changing into ultra-high efficiency building equipment would deliver the most amount of reduction.

• Journal of the Architectural Institute of Korea Structure & Construction
• /
• v.34 no.9
• /
• pp.27-34
• /
• 2018

The purpose of this study is to demonstrate and analyze the function of a Smart Plug before and after it is applied on the electrical appliances by controlling standby power usage. The research measures and analyzes the amount of electrical energy used while activating the Smart Plug with two types of appliances in a university facilities. The smart plugs were applied into a Group 1 appliances (Multi-function device, computer, laptop, Air con) which completely hinder the standby power, and a Group 2 appliances (Refrigerator, cold and hot water dispenser) which does not completely hinder the standby powers due to the characteristics of the function. First, the total standby power saving of all electrical appliances (Group 1 and Group 2) using the Smart plug was measured as 4.59%. Second, the energy saving of the Group 1 products was analyzed as 26.43%. Third, the standby power saving of the air conditioners from mid October to early December was measured as 31.06%, during the seasons when air conditioning was not actively in use. The research indicates that all specified appliances did have better energy efficiency with the Smart plug regardless of the amount of energy usage.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.401-407
• /
• 2022

Central heating, ventilation and air conditioning (HVAC) is one of the largest consumers of energy in the residential sector. This project explores the use of multiple HVAC units and/or Zoning in a single residence to reduce energy loads. The energy consumption data of a detached single-family home using two HVAC units, one primary for the main house and a secondary HVAC for a casita, was collected for the same month for two consecutive years, along with details related to the outdoor temperature and the square footage being air-conditioned by each HVAC. A regression algorithm was trained using the above details to find the relation between the parameters. Next, based on the occupancy and usage patterns, the HVAC was redesigned assuming more area under the secondary HVAC unit. The trained algorithm was then used to make energy usage predictions for the revised primary HVAC area, with the assumption that the secondary HVAC unit was turned off. The results were compared with existing energy usage data. It was determined that there were significant energy savings in the second scenario. It is expected that this study and its findings will help future research projects explore more ideas as alternatives to central HVAC, in improving the economic viability of existing options, and in developing a savings calculation tool that will help consumers make informed decisions on their best alternatives to central HVAC.