• Journal of the Korean Solar Energy Society
• /
• v.25 no.3
• /
• pp.11-18
• /
• 2005

The Korea Institute of Energy Research(KIER) has begun collecting horizontal global insolation data since May, 1982 at different locations. Because of a poor reliability of existing data, KIER's new data will be extensively used by the solar system users as well as by research institutes. But the quality of solar insolation data is not always good. This reports on an attempt to identify systematic error in such data using clear-day analysis for data rehabilitation. Clear-day analysis is successful in uncovering solar insolation data of questionable quality. It is not proven that rehabilitation process can improve the quality of data for daily or monthly means, but it is suggested that the method can be used to improve the quality of data for monthly means of several years for use in many applications of solar energy planning. Earlier studies finding a maximum ETR of about 0.80 are confirmed.

• Journal of Information Processing Systems
• /
• v.16 no.2
• /
• pp.460-477
• /
• 2020

As one of the most significant challenges in the virtual data center, the virtual data center embedding has attracted extensive attention from researchers. The existing research works mainly focus on how to design algorithms to increase operating revenue. However, they ignore the energy consumption issue of the physical data center in virtual data center embedding. In this paper, we focus on studying the energy-aware virtual data center embedding problem. Specifically, we first propose an energy consumption model. It includes the energy consumption models of the virtual machine node and the virtual switch node, aiming to quantitatively measure the energy consumption in virtual data center embedding. Based on such a model, we propose two algorithms regarding virtual data center embedding: one is heuristic, and the other is based on particle swarm optimization. The second algorithm provides a better solution to virtual data center embedding by leveraging the evolution process of particle swarm optimization. Finally, experiment results show that our proposed algorithms can effectively save energy while guaranteeing the embedding success rate.

• International Journal of High-Rise Buildings
• /
• v.2 no.4
• /
• pp.285-291
• /
• 2013

This paper proposes a framework for understanding the energy consumption differences between tall and low-rise buildings. Energy usage data from 706 office buildings in New York illustrates expected correlations from the framework. Notable correlations include: taller buildings tend to use more energy until a plateau at 30~39 floors; tall buildings in Manhattan use 20% more energy than low-rise buildings in Manhattan, while tall buildings outside Manhattan use 4% more energy than low-rise buildings outside Manhattan. Additional correlations are discussed, among which is the trend that the Energy Star program in New York City assigns higher ratings to tall buildings with higher EUIs than low-rise buildings with the same EUI. Since Energy Star is based on regressions of existing buildings, the Energy Star ratings suggest taller buildings have higher EUIs than shorter buildings, which is confirmed by the New York City energy benchmarking data.

• Journal of Environmental Science International
• /
• v.23 no.3
• /
• pp.493-502
• /
• 2014

EnergyPlus is a whole building energy simulation program that engineers, architects, and researchers use to model energy and water use in buildings. Modeling the performance of a building with EnergyPlus enables building professionals to optimize the building design to use less energy and water. This program provides energy analysis of building and needs weather data for simulation. Weather data is available for over 2,000 locations in a file format that can be read by EnergyPlus. However, only five locations are avaliable in Korea. This study intends to use AWS data for having high spatial resolution to simulate building energy. The result of this study shows the possibility of using AWS data for energy simulation of building.

• New & Renewable Energy
• /
• v.19 no.4
• /
• pp.98-107
• /
• 2023

A study was conducted to analyze data from 1981 to 2020 for understanding the impact of climate on solar energy generation. A significant increase of 104.6 kWhm^-2 was observed in the annual cumulative solar radiation over this period. Notably, the distribution of solar radiation shifted, with the solar radiation in Busan rising from the seventh place in 1981 to the second place in 2020 in South Korea. This study also examined the correlation between long-term temperature trends and solar radiation. Areas with the highest solar radiation in 2020, such as Busan, Gwangju, Daegu, and Jinju, exhibited strong positive correlations, suggesting that increased solar radiation contributed to higher temperatures. Conversely, regions like Seosan and Mokpo showed lower temperature increases due to factors such as reduced cloud cover. To evaluate the impact on solar energy production, simulations were conducted using climate data from both years. The results revealed that relying solely on historical data for solar energy predictions could lead to overestimations in some areas, including Seosan or Jinju, and underestimations in others such as Busan. Hence, considering long-term climate variability is vital for accurate solar energy forecasting and ensuring the economic feasibility of solar projects.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.5
• /
• pp.1141-1154
• /
• 2015

This paper describes the convergence of the energy technology and information and communication technology (ICT), which helps to consume less energy effectively. While a lot of researches have done against the increase of world energy usage, most of them focus on the efficiency of energy supply, transfer, and consumption equipment. Applying the ICT to decrease energy usage could help to find energy saving factors in the new field that has not been considered as a valuable one before. The big data service with the energy technology and ICT convergence enables correlation analyses of large sets of energy and environmental data. Finding a data tendency with a big data service helps to develop energy saving policies. Furthermore, it could make a further step to develop a new business model. This paper introduces the real cases of the company and project that provides a big data service with the ICT convergence.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.5
• /
• pp.2029-2046
• /
• 2016

Energy consumption is an important index in mobile ad hoc networks. Data packet transmission increases among nodes, particularly in big data communication. However, nodes may be unable to transmit data packets because of energy over-consumption. Consequently, information may be lost and network communication may block. While opportunistic network is a kind of mobile ad hoc networks, researchers do not focus on energy consumption in opportunistic network communication. This study proposed an effective sensor energy supply scheme that can be applied in opportunistic networks. This scheme considers nodes sensor requests and communication model. In this scheme, nodes do not only accomplish energy supply in communication, but also extend communication time in opportunistic networks. Compared with the Spray and Wait algorithm and the Binary Spray and Wait algorithm in simulations, the proposed scheme extends communication time, increases data packet transmission, and accomplishes energy supply among nodes.

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.5
• /
• pp.81-88
• /
• 2017

Solar-powered wireless sensor nodes can use extra energy to obtain additional data to increase the precision. However, if the amount of data sensed is increased indiscriminately, the overhead of relay nodes may increase, and their energy may be exhausted. In this paper, we introduce a sensing and compression rate selection scheme to increase the amount of data obtained while preventing energy exhaustion. In this scheme, the neighbor nodes of the sink node determine the limit of data to be transmitted according to the allocated energy and their descendant nodes, and the other nodes select a compression algorithm appropriate to the allocated energy and the limitation of data to be transmitted. A simulation result verifies that the proposed scheme gathers more data with a lower number of blackout nodes than other schemes. We also found that it adapts better to changes in node density and the amount of energy harvested.

• Journal of the Korean Solar Energy Society
• /
• v.34 no.1
• /
• pp.8-18
• /
• 2014

Building simulation is used in a variety of sectors. In its early years, building simulation was mainly used in the design phase of a building for basic functions. Recently, however, it has become increasingly important during the operating phase, for commissioning and facility management. Most building simulation tools are used to estimate the thermal environment and energy consumption performance, and hence, they require the inputting of hourly weather data. A building simulation used for prediction should take into account the use of standard weather data. Weather data, which is used as input for a building simulation, plays a crucial role in the prediction performance, and hence, the selection of appropriate weather data is considered highly important. The present study proposed a technique for generating real-time weather data files, as opposed to the standard weather data files, which are required for running the building simulation. The forecasted weather elements provided by the Korea Meteorological Administration (KMA), the elements produced by the calculations, those utilizing the built-in functions of Energy Plus, and those that use standard values are combined for hourly input. The real-time weather data files generated using the technique proposed in the present study have been validated to compare with measured data and simulated data via EnergyPlus. The results of the present study are expected to increase the prediction accuracy of building control simulation results in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.10
• /
• pp.4681-4702
• /
• 2016

Recently, Massive energy consumption in Cloud Storage System has attracted great attention both in industry and research community. However, most of the solutions utilize single method to reduce the energy consumption only in one aspect. This paper proposed an energy effective gear-shifting mechanism (E2GSM) in Cloud Storage System to save energy consumption from multi-aspects. E2GSM is established on data classification mechanism and data replication management strategy. Data is classified according to its properties and then be placed into the corresponding zones through the data classification mechanism. Data replication management strategies determine the minimum replica number through a mathematical model and make decision on replica placement. Based on the above data classification mechanism and replica management strategies, the energy effective gear-shifting mechanism (E2GSM) can automatically gear-shifting among the nodes. Mathematical analytical model certificates our proposed E2GSM is energy effective. Simulation experiments based on Gridsim show that the proposed gear-shifting mechanism is cost effective. Compared to the other energy-saved mechanism, our E2GSM can save energy consumption substantially at the slight expense of performance loss while meeting the QoS of user.