• New & Renewable Energy
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• v.20 no.1
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• pp.95-101
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• 2024

Since 1990, the Renewable Big Data Research Lab at the Korea Institute of Energy Technology has been observing solar radiation at 16 sites across South Korea. Serving as the National Reference Standard Data Center for Renewable Energy since 2012, it produces essential data for the sector. By 2020, it standardized meteorological year data from 22 sites. Despite user demand for data from approximately 260 sites, equivalent to South Korea's municipalities, this need exceeds the capability of measurement-based data. In response, our team developed a method to derive solar radiation data from satellite images, covering South Korea in 400,000 grids of 500 m × 500 m each. Utilizing satellite-derived data and ERA5-Land reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF), we produced standard meteorological year data for 1,000 sites. Our research also focused on data measurement traceability and uncertainty estimation, ensuring the reliability of our model data and the traceability of existing measurement-based data.

• Journal of the Korean Solar Energy Society
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• v.31 no.1
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• pp.51-58
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• 2011

The energy makes the basic element which improves the quality of life with motive power of industry and life. However, using the fossil fuel resources was restricted through it's abuse and exhaustion, and that cause a global warming resultingly. According to the reason, the world increased the interest that are stability and use of new and renewable energy which is clean energy with environment. Therefore, the property data of new and renewable is needed for developing and supplying the energy. In other words, the data of new and renewable energy becomes the standards for supply and evaluation of new and renewable energy with development of industry and technology. Also, the necessity came to the fore as the reference and standards of new and renewable energy data. Therefore, in this study, we evaluate and collect the solar radiation data as the new and renewable data and process the collected data through the standards for valuation. We evaluate uncertainty with standards which are NREL, WMO, and GUM. Whereby the data becomes reference standards data and gains the credibility. For the reliability data, we correct the measuring instrument with correction period. Using the DQMS and SERI QC, we efficiently manage and evaluate the solar radiation data. As a result, we evaluate uncertainty as 1,120 case about 16 area. we achieve credibility of data from evaluated solar radiation data and provide an accurate information to user. The annual average of horizontal radiation presents between 1,484 and 4,577, then the uncertainty evaluates from 163 to 453. The error of uncertainty presents smaller than the measurement values. So, we judge a credibility of data by expression of reliability quantitatively. In additional, the reference standards data which is possible to approach anywhere will be used for the supporting related industry and policy making.

• New & Renewable Energy
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• v.20 no.1
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• pp.88-94
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• 2024

This study explored the potential and implementation of renewable energy sources in Sri Lanka, focusing on the theoretical potential of solar and wind energy to develop self-reliant energy models. Using advanced climate data from the European Centre for Medium-Range Weather Forecasts and Global Solar/Wind Atlas provided by the World Bank, we assessed the renewable energy potential across Sri Lanka. This study proposes off-grid and minigrid systems as viable solutions for addressing energy poverty in rural regions. Rural villages were classified based on solar and wind resources, via which we proposed four distinct energy self-reliance models: Renewable-Dominant, Solar-Dominant, Wind-Dominant, and Diesel-Dominant. This study evaluates the economic viability of these models considering Sri Lanka's current energy market and technological environment. The outcomes highlight the necessity for employing diversified energy strategies to enhance the efficiency of the national power supply system and maximize the utilization of renewable resources, contributing to Sri Lanka's sustainable development and energy security.

• KIEAE Journal
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• v.12 no.2
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• pp.77-82
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• 2012

Data mining is to discover problems in the large amounts of data. Also, data mining trying to find the cause of the problem and the structure. Building energy consumption patterns, the amount of data is infinite. Also, the patterns have a lot of direct and indirect effects. Discussion is needed about the correlation. This work looking for the cause of energy consumption. As a result, energy management can find out the issue. Building energy analysis utilizing data mining techniques to predict energy consumption. And the results are as follows: 1) Using data mining technique, We classified complicated data to several patterns and gained meaningful informations from them. 2) Using cluster analysis, We classified building energy consumption data of residents and analyzed characters of patterns.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.267-273
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• 2013

Early design phase energy modeling is used to provide the design team with first order of magnitude feedback about the impact of various building configurations. For better energy-conscious and sustainable building design and operation, the construction of BIM data interoperability for energy performance assessment in the early design phase is important. The purpose of this study is to suggest construction of BIM data interoperability for energy performance assessment based on BIM. To archive this purpose, the authors have investigated advantage of BIM-based energy performance assessment through comparison with traditional energy performance assessment and suggested requirement for construction of open BIM environment such as BIM data creation, BIM data software practical use, BIM data application and verification. In addition, the authors have suggested BIM data interoperability and BIM energy property mapping method focused on materials.

• KIEAE Journal
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• v.17 no.2
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• pp.13-20
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• 2017

Meteorological data is one of the important factors in the calculation of building energy demand. The purposes of this study are to review the limitations of the typical meteorological data of ECO2 program and to create the new typical meteorological data and then analyze the building energy demands for additional regions which are not included in the existing 13 region in the ECO2 program. The extended typical meteorological data to a total of 33 regions were based on IWEC(International Weather for Energy Calculations) data files and were created in the form applicable to the building energy efficiency rating certification system. As a result of comparing the heating energy demands of a representative region with the surrounding regions in each of five regions in Korea, the variance of Cv(RMSE) ranged from 36% to 344% and MBE ranged from -32% to 190% for the whole regions. This suggests that the difference of heating energy demand may vary greatly depending on the region where the meteorological data is used and the meteorological data of more detailed regions is needed for reliable calculation of building energy demand.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.2
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• pp.301-307
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• 2021

After the spread of the data collection devices such as smart meters, energy data is increasingly collected in a variety of ways, and its importance continues to grow. However, due to technical or practical limitations, errors such as missing or outliers in the data occur during data collection process. Especially in the case of customer-related data, billing problems may occur, so energy companies are conducting various research to process such data. In addition, efforts are being made to create added value from data, which makes it difficult to provide such services unless reliability of data is guaranteed. In order to solve these challenges, this research analyzes prior research related to bad data processing specifically in the energy field, and propose new missing value processing methods to improve the reliability and field utilization of energy data.

• Journal of the Korean Solar Energy Society
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• v.31 no.3
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• pp.89-94
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• 2011

In this study, we measured the wind data as new and renewable energy resources and carried out the evaluation of uncertainty about these data with the authentic standards. These data collected at the 20 locations in korea. We carried out the processing and evaluation about these data with standards as ISO, GUM, and IEC. Whereby these data become standards data and the credibility are gained. These data include some information as direction, humidity, pressure, temperature, and energy density. The annual average of wind speed(in Hamo) was measured as 9.5m/s, then the uncertainty was evaluated as ${\pm}0.88m/s$. We judge the credibility of data by expression of reliability quantitatively. In additional, the standards data is able to approach anywhere and it will be used to support of related research and industry.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.559-563
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• 2015

Identification of energy use patterns in buildings has a great opportunity for energy saving. To find what energy use patterns exist, clustering analysis has been commonly used such as K-means and hierarchical clustering method. In case of high dimensional data such as energy use time-series, data reduction should be considered to avoid the curse of dimensionality. Principle Component Analysis, Autocorrelation Function, Discrete Fourier Transform and Discrete Wavelet Transform have been widely used to map the original data into the lower dimensional spaces. However, there still remains an ongoing issue since the performance of clustering analysis is dependent on data type, purpose and application. Therefore, we need to understand which data reduction techniques are suitable for energy use management. This research aims find the best clustering method using energy use data obtained from Seoul National University campus. The results of this research show that most experiments with data reduction techniques have a better performance. Also, the results obtained helps facility managers optimally control energy systems such as HVAC to reduce energy use in buildings.

• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.115-122
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• 2017

In this paper, we propose an adaptive data aggregation and compression scheme for wireless sensor networks with energy-harvesting nodes, which increases the amount of data arrived at the sink node by efficient use of the harvested energy. In energy-harvesting wireless sensor networks, sensor nodes can have more than necessary energy because they harvest energy from environments continuously. In the proposed scheme, when a node judges that there is surplus energy by estimating its residual energy, the node compresses and transmits the aggregated data so far. Conversely, if the residual energy is estimated to be depleted, the node turns off its transceiver and collects only its own sensory data to reduce its energy consumption. As a result, this scheme increases the amount of data collected at the sink node by preventing the blackout of relay nodes and facilitating data transmission. Through simulation, we show that the proposed scheme suppresses the occurrence of blackout nodes and collect the largest amount of data at the sink node compared to previous schemes.