• New & Renewable Energy
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• v.20 no.1
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• pp.102-109
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• 2024

The Typical Meteorological Year (TMY) dataset compiles 12 months of data that best represent long-term climate patterns, focusing on global horizontal irradiance and other weather-related variables. However, the irradiance measured on the plane of the array (POA) shows certain distinct distribution characteristics compared with the irradiance in the TMY dataset, and this may introduce some biases. Our research recalculated POA irradiance using both the Isotropic and DIRINT models, generating an updated dataset that was tailored to POA characteristics. Our analysis showed a 28% change in the selection of typical meteorological months, an 8% increase in average irradiance, and a 40% reduction in the range of irradiance values, thus indicating a significant shift in irradiance distribution patterns. This research aims to inform stakeholders about accurate use of TMY datasets in potential decision-making. These findings underscore the necessity of creating a typical dataset by using the time series of POA irradiance, which represents the orientation in which PV panels will be deployed.

• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.166.2-166.2
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• 2011

Incheon Bay of Korea is one of the most famous regions for high tidal range. Ministry of Land, Transport and Maritime Affairs(MLTM) has implemented preliminary investigation for tidal power energy development in this area since 2006. Through field observations, various kinds of marine data consisting of depth and geography, marine weather, tidal currents, wave, seawater characteristics, geology, marine ecosystem and marine environment were gathered. To use these data efficiently for the determining of feasibility of developing and appropriateness of project scale, spatial data management and application system is essential. Therefore, in this study, the concept, methodology and procedure of spatial data modeling are defined for tidal energy development. Spatial data modeling consists of essential model relating to tidal energy directly and optional model including environmental factors. Essential model is composed with fundamental elements like as depth, geography, and several numerical modeling results(tide, tidal current, wave).

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.25-33
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• 2021

Energy-harvesting wireless sensor networks(EH-WSNs) can collect energy from the environment and overcome the technical limitations of existing power. Since the transmission distance in a wireless sensor network is limited, the data are delivered to the destination node through multi-hop routing. In EH-WSNs, the routing protocol should consider the power situations of nodes, which is determined by the remaining power and energy-harvesting rate. In addition, in applications such as environmental monitoring, when there are urgent data, the routing protocol should be able to transmit it stably and quickly. This paper proposes an adaptive routing protocol that satisfies different requirements of normal and urgent data. To extend network lifetime, the proposed routing protocol reduces power imbalance for normal data and also minimizes transmission latency by controlling the transmission power for urgent data. Simulation results show that the proposed adaptive routing can improve network lifetime by mitigating the power imbalance and greatly reduce the transmission delay of urgent data.

• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.705-707
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• 2005

For the development of geothermal energy, many different kind of geoscientific data including both surface geological data and underground geomechanical data, are acquired. Integration of such data itself for better understanding of underground condition is not a simple process due to complexity of the data, i.e. mixture of 20 and 3D data, mixture of geological data, geochemical data, geomechanical data and hydrogeological data. This paper reports a preliminary suggestion of GIS modeling for such specific purpose. Data used for GIS modeling mainly came from British case studies. The modeling is much more focused on the design of database for 3D underground geotechnical data in this study.

• Speech Sciences
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• v.13 no.1
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• pp.129-139
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• 2006

In this paper, we propose two effective energy feature normalization methods for robust speech recognition in noisy environments. In the first method, we estimate the noise energy and remove it from the noisy speech energy. In the second method, we propose a modified algorithm for the Log-energy Dynamic Range Normalization (ERN) method. In the ERN method, the log energy of the training data in a clean environment is transformed into the log energy in noisy environments. If the minimum log energy of the test data is outside of a pre-defined range, the log energy of the test data is also transformed. Since the ERN method has several weaknesses, we propose a modified transform scheme designed to reduce the residual mismatch that it produces. In the evaluation conducted on the Aurora2.0 database, we obtained a significant performance improvement.

• Journal of the Korean Solar Energy Society
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• v.37 no.1
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• pp.71-80
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• 2017

A Data center houses a large number of server computers, storage and etc in racks. With the rapid increase of heat generation rates per rack in a data center, energy consumption rates for cooling have been increased year by year. In this study, energy conservation effects of a MOA (multi-stage outdoor air enabled) cooling system in a data center has been investigated when it is applied to 5 different locations, Korea. As results, Energy conservation effects of the MOA cooling system was achieved at about 20% to 30%. Humidifier operation time was 40 to 55 days when supply air temperature was maintained at 13, and humidity condition was kept within the allowed range even though humidifier was off. Furthermore, humidification was not needed when supply air temperature was maintained at $25^{\circ}C$. In selected 5 locations in Korea, the difference of regional climatic conditions affected no more than 5% in cooling energy consumption rates.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.626-630
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• 2019

After several significant nuclear accidents, public attitudes toward nuclear energy technologies and facilities are considered to be one of the essential factors in the national energy and electricity policy-making process of several nations that employ nuclear energy as their key energy resource. However, it is difficult to explore and capture such an attitude, because the majority of prior studies analyzed public attitudes with a limited number of respondents and fragmentary opinion polls. In order to supplement this point, this study suggests a big data analyzing method with K-LIWC (Korean-Linguistic Inquiry and Word Count), sentiment and query analysis methods, and investigates public attitudes, positive and negative emotional statements about nuclear energy with the collected data sets of well-known social media and network services in Korea over time. Results show that several events and accidents related to nuclear energy have consistent or temporary effects on the attitude and ratios of the statements, depending on the kind of events and accidents. The presented methodology and the use of big data in relation to the energy industry is suggested as it can be helpful in addressing and exploring public attitudes. Based on the results, implications, limitations, and future research areas are presented.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.36 no.5
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• pp.165-176
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• 2020

The purpose of this study is to develop a estimation method of energy consumption by end-use in office buildings. For this, the current status of information on building energy use was investigated, and the domestic and foreign literature on the classification of energy use in non-residential buildings and the estimation method of energy use were reviewed. In addition, the characteristics of energy consumption by end-use were analyzed with measurement data of 48 office buildings in Seoul. As results, the annual and monthly estimation method of energy consumption by end-use in office buildings using public and measurement data was presented, and the applicability of the estimation method was examined by applying to sample office buildings.

• ETRI Journal
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• v.42 no.3
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• pp.341-350
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• 2020

In a wireless sensor network (WSN), the data transmission technique based on the cooperative multiple-input multiple-output (CMIMO) scheme reduces the energy consumption of sensor nodes quite effectively by utilizing the space-time block coding scheme. However, in networks with high node density, the scheme is ineffective due to the high degree of correlated data. Therefore, to enhance the energy efficiency in high node density WSNs, we implemented the distributed source coding (DSC) with the virtual multiple-input multiple-output (MIMO) data transmission technique in the WSNs. The DSC-MIMO first compresses redundant source data using the DSC and then sends it to a virtual MIMO link. The results reveal that, in the DSC-MIMO scheme, energy consumption is lower than that in the CMIMO technique; it is also lower in the DSC single-input single-output (SISO) scheme, compared to that in the SISO technique at various code rates, compression rates, and training overhead factors. The results also indicate that the energy consumption per bit is directly proportional to the velocity and training overhead factor in all the energy saving schemes.

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