• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.8
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• pp.1111-1115
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• 2014

With the increasing interest of a demand side management using a Smart Grid infrastructure, the demand resources and energy usage data management becomes an important factor in energy industry. In addition, with the help of Advanced Measuring Infrastructure(AMI), energy usage data becomes a Big Data System. Therefore, it becomes difficult to store and manage the demand resources big data using a traditional relational database management system. Furthermore, not many researches have been done to analyze the big energy data collected using AMI. In this paper, we are proposing a Hadoop based Big Data system to manage the demand resources energy data and we will also show how the demand side management systems can be used to improve energy efficiency.

• KIISE Transactions on Computing Practices
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• v.24 no.1
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• pp.24-32
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• 2018

This paper proposes a Big Data system for energy Big Data which is aggregated in real-time from industrial and public sources. The constructed Big Data system is based on Hadoop and the Spark framework is simultaneously applied on Big Data processing, which supports in-memory distributed computing. In the paper, we focus on Big Data, in the form of heat energy for district heating, and deal with methodologies for storing, managing, processing and analyzing aggregated Big Data in real-time while considering properties of energy input and output. At present, the Big Data influx is stored and managed in accordance with the designed relational database schema inside the system and the stored Big Data is processed and analyzed as to set objectives. The paper exemplifies a number of heat demand plants, concerned with district heating, as industrial sources of heat energy Big Data gathered in real-time as well as the proposed system.

• 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.

• Convergence Security Journal
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• v.22 no.1
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• pp.55-60
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• 2022

This study is to analyze trends and relational structures in the energy sector related to renewable energy. For this reason, in this study, we focused on big data including SNS data. SNS utilizes the Instagram platform to collect renewable energy hash tags and use them as a word embedding method for big data analysis and social network analysis, and based on the results derived from this research, it will be used for the development of the renewable energy industry. It is expected that it can be utilized.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.287-296
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• 2024

Artificial intelligence (AI), the Internet of Things (IoT), and Big Data are playing important roles in improving or upgrading energy efficiency. Furthermore, their roles in energy efficiency are expected to become more and more essential. This study conducted a bibliometric comparative analysis on the features in the articles on the AI, the IoT, and the Big Data in energy efficiency by using the Web of Science database and compared the features in their trends in article publications, citations, countries, research areas, journals, and funding agencies from 2012 to 2022. This study attempted to make significant contributions by shedding new light on the following features. Among the AI, the IoT, and the Big Data in energy efficiency, the most articles were published and the most article citations were received in the AI in energy efficiency. China was found out to be the most leading country. Engineering and computer science were revealed to be the first research area. IEEE Access and IEEE Internet of Things were ranked with first journal. National Natural Science Foundation of China was the first research funding agency concerning the articles published in the AI, the IoT, and the Big Data in energy efficiency from 2012 to 2022.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.82-86
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• 2019

We aim to use public data different from the remote BEMS energy diagnostics technology and already established and then switch the conventional operation environment to a big-data-based integrated management environment to operate and build a building energy management environment of maximized efficiency. In Step 1, various network management environments of the system integrated with a big data platform and the BEMS management system are used to collect logs created in various types of data by means of the big data platform. In Step 2, the collected data are stored in the HDFS (Hadoop Distributed File System) to manage the data in real time about internal and external changes on the basis of integration analysis, for example, relations and interrelation for automatic efficient management.

• 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 Data and Information Science Society
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• v.26 no.5
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• pp.1141-1154
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• 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)
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• v.10 no.5
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• pp.2029-2046
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• 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.

• 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.