• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.15 no.4
• /
• pp.287-293
• /
• 2003

This study examined the temperature-dependent regression model of energy consumption based on various measuring period. The methodology employed was to construct temperature-dependent linear regression model of daily energy consumption from one day to three months data-sets and to compare the annual heating energy consumption predicted by these models with actual annual heating energy consumption. Heating energy consumption from a building in Daejon was examined experimentally. From the results, predicted value based on one day experimental data can have error over 100%. But predicted value based on one week experimental data showed error over 30%. And predicted value based on over three months experimental data provides accurate prediction within 6% but it will be required very expensive.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.13 no.10
• /
• pp.966-973
• /
• 2001

In this study, measurement and analysis of energy consumption of an office building with cleanroom and laboratory have been conducted. Data acquisition system for collecting energy consumption data of the whole building including air-conditioning equipments has been installed in a building located in Seoul. Data are collected for a period of one year in 2000 and analyzed for studying the energy consumption pattern. The percentage of electrical energy used for air-conditioning system is measured to be 46.1%. The collected data will serve as valuable information for diagnosing and improving the energy system of the building.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
• /
• v.17 no.3
• /
• pp.21-29
• /
• 2021

In this study, the current regulation of the water source energy, one of the renewable energy, was analyzed, and the improvement plan for the high efficient data center cooling system was suggested. In the improvement plan, the design and construction guidelines of the water source energy system permit to adopt the cooling and heating system with or without heat pump. In addition, it should also include the system operated in the cooling mode only all year-round. The domestic test standards to consider the water source operating conditions should be developed. Especially, it is highly recommended that the test standards to include the system with forced cooling and free cooling modes related with the enhanced data center cooling system adopting the water source energy.

• Journal of Internet Computing and Services
• /
• v.9 no.5
• /
• pp.131-140
• /
• 2008

In this paper, we present a data dissemination protocol that guarantees energy-efficient data transmission and maximizes network lifetime. SPMS that outperforms the well-known protocol SPIN uses the shortest path to minimize the energy consumption. However, since it repeatedly uses the same path, maximizing the network lifetime is impossible. In this paper, we propose a protocol for data dissemination called the protocol Considering Between Energy and Distance (ConBED). It solves the network lifetime problem using the residual energy and the distance between nodes to determine a path for data dissemination. The simulation results show that ConBED guarantees energy-efficient transmission and increases the network lifetime by approximately 69% than that of SPMS.

• Journal of Korea Multimedia Society
• /
• v.22 no.3
• /
• pp.366-373
• /
• 2019

The development of monitoring and analysis systems to increase productivity while saving energy is needed as a method to reduce huge amount of energy consumed in the process of producing large forged products. In this paper, we propose a system to monitor and analyze energy usage in real-time collected from gas-meter, wattmeter, and thermometer based on IoT installed in forging factories. The system consists of a data collection server for collecting and processing data from IoT- based platform and existing SCADA equipment and ERP/MES system in forging factories, and an application server for providing services to users. To develop the system, the overall system structure is logically diagrammed, and the databases configuration and implementation modules to efficiently store and manage data are presented. In the future, the system will be utilized to reduce energy consumption by analyzing energy usage pattern and optimizing process works with real-time energy usage and production process data for each facility.

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

• Journal of the Korea Convergence Society
• /
• v.9 no.11
• /
• pp.253-262
• /
• 2018

Recently, the energy industry is implementing renewable energy supply policy to reduce energy consumption. The purpose of this study is to build a database that can help promote the supply of geothermal energy system to prepare for the increase of renewable energy demand and to develop a method to evaluate the possibility of geothermal energy system installation by using database information. The data used in the study was reliable using open data provided by national agencies. We obtained information necessary for the possibility of geothermal energy system installation, constructed a dedicated database, and studied the method of calculating the geothermal well capacity by using the database information. In the future, this study will establish a local environmental evaluation standard and add information on other renewable energy to contribute to the activation of renewable energy supply.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.6
• /
• pp.1957-1980
• /
• 2021

The significant aim of this research has always been to evaluate the mechanism for efficient and inherently aware usage of vitality in-home devices, thus improving the information of smart metering systems with regard to the usage of selected homes and the time of use. Advances in information processing are commonly used to quantify gigantic building activity data steps to boost the activity efficiency of the building energy systems. Here, some smart data mining models are offered to measure, and predict the time series for energy in order to expose different ephemeral principles for using energy. Such considerations illustrate the use of machines in relation to time, such as day hour, time of day, week, month and year relationships within a family unit, which are key components in gathering and separating the effect of consumers behaviors in the use of energy and their pattern of energy prediction. It is necessary to determine the multiple relations through the usage of different appliances from simultaneous information flows. In comparison, specific relations among interval-based instances where multiple appliances use continue for certain duration are difficult to determine. In order to resolve these difficulties, an unsupervised energy time-series data clustering and a frequent pattern mining study as well as a deep learning technique for estimating energy use were presented. A broad test using true data sets that are rich in smart meter data were conducted. The exact results of the appliance designs that were recognized by the proposed model were filled out by Deep Convolutional Neural Networks (CNN) and Recurrent Neural Networks (LSTM and GRU) at each stage, with consolidated accuracy of 94.79%, 97.99%, 99.61%, for 25%, 50%, and 75%, respectively.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.1256-1263
• /
• 2022

With the advent of the Fourth Industrial Revolution, the amount of energy used in buildings has been increasing due to changes in the energy use structure caused by the massive spread of information-oriented equipment, climate change and greenhouse gas emissions. For the efficient use of energy, it is necessary to have a plan that can predict and reduce the amount of energy use according to the type of energy source and the use of buildings. To address such issues, this study presents a model embedded in a digital twin that predicts energy use in buildings. The digital twin is a system that can support a solution of urban problems through the process of simulations and analyses based on the data collected via sensors in real-time. To develop the energy use prediction model, energy-related data such as actual room use, power use and gas use were collected. Factors that significantly affect energy use were identified through a correlation analysis and multiple regression analysis based on the collected data. The proof-of-concept prototype was developed with an exhibition facility for performance evaluation and validation. The test results confirm that the error rate of the energy consumption prediction model decreases, and the prediction performance improves as the data is accumulated by comparing the error rates of the model. The energy use prediction model thus predicts future energy use and supports formulating a systematic energy management plan in consideration of characteristics of building spaces such as the purpose and the occupancy time of each room. It is suggested to collect and analyze data from other facilities in the future to develop a general-purpose energy use prediction model.

• Journal of the Korean Solar Energy Society
• /
• v.30 no.2
• /
• pp.54-64
• /
• 2010

Interest in new and renewable energies like solar energy and wind energy is increasing throughout the world due to the rapidly expanding energy consumption and environmental reasons. An essential requirement for wind force power generation is estimating the size of wind energy accurately. Wind energy is estimated usually using meteorological data or field measurement. This study attempted to estimate wind energy density using meteorological data on daily mean wind speed and the Weibull parameters in Seoul, a representative inland city where over 60% of 15 story or higher apartments in Korea are situated, and Busan, Incheon, Ulsan and Jeju that are major coastal cities in Korea. According to the results of analysis, the monthly mean probability density distribution based on the daily mean wind speed agreed well with the monthly mean probability density distribution based on the Weibull parameters. This finding suggests that the Weibull parameters, which is highly applicable and convenient, can be utilized to estimate the wind energy density distribution of each area. Another finding was that wind energy density was higher in coastal cities Busan and Incheon than in inland city Seoul.