• Solar Energy
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• v.20 no.1
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• pp.31-43
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• 2000

The Korea Institute of Energy Research(KIER) has been collecting horizontal global radiation data since May, 1982 for 16 different locations. KIER's new data is expected to be extensively used by designer and researchers of solar systems in lieu of unreliable old ones. Unfortunately, the quality of the data has not always been properly mentioned. Some of them were taken at temporary field stations where the primary goal of the measurement was quick estimation of local solar radiation. The purpose of this study is to systematically identify errors in such data set using clear-day analysis in an effort to rehabilitate error-ridden old data. Clear-day analysis successfully uncovered solar radiation data that had questionable quality. Even through the rehabilitation process not necessarily improves the quality of data for daily or monthly mean, it can be used to improve the quality of data for monthly means of several years and the processed data can be used in various applications of solar energy with more confidence. A average ETR value of 0.63 obtained in this study is in good agreement with previous results obtained by other researchers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3151-3168
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• 2021

Internet of Things (IoT) connects several objects with embedded sensors and they are capable of exchanging information between devices to create a smart environment. IoT smart devices have limited resources, such as batteries, computing power, and bandwidth, but comprehensive sensing causes severe energy restrictions, lowering data quality. The main objective of the proposal is to build a hybrid protocol which provides high data quality and reduced energy consumption in IoT sensor network. The hybrid protocol gives a flexible and complete solution for sensor selection problem. It selects a subset of active sensor nodes in the network which will increase the data quality and optimize the energy consumption. Since the unused sensor nodes switch off during the sensing phase, the energy consumption is greatly reduced. The hybrid protocol uses Dijkstra's algorithm for determining the shortest path for sensing data and Ant colony inspired variable path selection algorithm for selecting active nodes in the network. The missing data due to inactive sensor nodes is reconstructed using enhanced belief propagation algorithm. The proposed hybrid method is evaluated using real sensor data and the demonstrated results show significant improvement in energy consumption, data utility and data reconstruction rate compared to other existing methods.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.25 no.6
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• pp.310-316
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• 2013

This study was designed to investigate a method for short-term, real-time energy demand prediction, to cope with changing loads for the effective operation and management of buildings. Through a case study, a novel methodology for real-time energy demand prediction with the use of weather forecasting data was suggested. To perform the input and output operations of weather data, and to calculate solar radiation and EnergyPlus, the BCVTB (Building Control Virtual Test Bed) was designed. Through the BCVTB, energy demand prediction for the next 24 hours was carried out, based on 4 real-time weather data and 2 solar radiation calculations. The weather parameters used in a model equation to calculate solar radiation were sourced from the weather data of the KMA (Korea Meteorological Administration). Depending on the local weather forecast data, the results showed their corresponding predicted values. Thus, this methodology was successfully applicable to anywhere that local weather forecast data is available.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.55-59
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• 2022

This paper introduce Renewable Energy forecasting technology, which is a part of renewable management system. Then, calculation method of dedicated transmission line's meteorological data to forecast renewable energy is suggested. As the case of dedicated transmission line, there is only power output data combined the number of renewable plants' output that acquired from circuit breakers. So it is need to calculate meteorological data for dedicated transmission line that matched combined power output data. this paper suggests two calculation method. First method is select the plant has the largest capacity, and use it's meteorological data as line meteorological data. Second method is average with weight that given according to plants' capacity. In case study, suggested methods are applied to real data. Then use calculated data to Renewable forecasting and analyze the forecasting results.

• Journal of the Korean Solar Energy Society
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• v.29 no.4
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• pp.15-21
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• 2009

Since the solar radiation is main input for sizing any solar photovoltaic system and solar thermal power system, it will be necessary to understand and evaluate the insolation data. The Korea Institute of Energy Research(KIER) has begun collecting horizontal global insolation data since May, 1982 and direct normal insolation data since December 1992 at 16 different locations. Because of a poor reliability of existing data, KIER's new data will be extensively used by solar energy system users as well as by research institutes. Among some significant results, the yearly averaged horizontal global insolation was turned out 3.60 kWh/$m^2$/day and the yearly mean 2.62 kWh/$m^2$/day of the direct normal insolation was evaluated for all days.

• Journal of the Korean Solar Energy Society
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• v.39 no.6
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• pp.137-151
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• 2019

This study estimated the available renewable market potential based on Levelized Cost Of Electricity and then assessed the renewable derived energy self-sufficiency for the unit of local government in South Korea. To calculate energy self-sufficiency, 1 km gridded market renewable generation and local government scale of final energy consumption data were used based on the market costs and statistics for the recent three years. The results showed that the estimated renewable market potentials were 689 TWh (Install capacity 829 GW, 128 Mtoe), which can cover 120% of power consumption. 55% of municipalities can fully replace the existing energy consumption with renewable energy generation and the surplus generation can compensate for the rest area through electricity trade. However, it was confirmed that, currently, 47% of the local governments do not fully consider all renewable energy sources such as wind, hydro and geothermal in establishing 100% renewable energy. The results of this study suggest that energy planning is decentralized, and this will greatly contribute to the establishment of power planning of local governments and close the information gap between the central government, the local governments, and the public.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.4
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• pp.243-249
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• 2016

Energy-harvesting nodes in wireless sensor networks(WSNs) can be exhausted due to a heavy workload even though they can harvest energy from their environment. On contrast, they can sometimes fully charged, thus waste the harvested energy due to the limited battery-capacity. In order to utilize the harvested energy efficiently, we introduce a selective data compression and transmission range control scheme for energy-harvesting nodes. In this scheme, if the residual energy of a node is expected to run over the battery capacity, the node spends the surplus energy to exploit the data compression or the transmission range expansion; these operations can reduce the burden of intermediate nodes at the expanse of its own energy. Otherwise, the node performs only basic operations such as sensing or transmitting so as to avoid its blackout time. Simulation result verifies that the proposed scheme gathers more data with fewer number of blackout nodes than other schemes by consuming energy efficiently.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.127-135
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• 2022

With the advent of high technologies such as the 4th Industrial Revolution and artificial intelligence and big data, efforts are being made to solve urban problems and improve the quality of life by applying new technologies in the smart city field. In addition, as carbon neutrality has emerged as an important issue due to global warming, smart city energy platform technologies such as urban energy management, efficiency improvement, and carbon reduction are in the spotlight. In order to effectively manage urban energy, energy resource information such as electricity, water, gas, hot water, heating, etc. must be collected from the management system of various energy utilities and managed on the central platform. The centrally integrated data is delivered to external city management systems that require city energy information through an energy platform. This study developed a CIM profile for smart city energy monitoring required to provide energy data to external systems. Electric data model were designed using the CIM class of IEC 61970, and water, gas, and heat data model were designed in compliance with the UML-based design ideas of IEC 61970.

• Solar Energy
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• v.18 no.3
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• pp.31-39
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• 1998

Selection of a right weather data set has been considered as one of important factors for a successful building energy audit process. A set of 30 year raw weather data base for six major cities has been developed to provide the weather data file for building energy audit and retrofit analysis in Korea. The program named as KWDP(KIER Weather Data Processor) uses the DB to produce a right data set for a specific building energy performance simulation program like DOE2.1E. A program called WMAKE has been developed to generate the right set of input parameters for DOE2.1E weather utility program. The set of the programs could provide the right weather data for specific building energy audit and retrofit analysis.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.851-858
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• 2021

Due to the increase in renewable energy and distributed resources, not only traditional data but also various energy-related data are being generated in the new energy industry. In other words, there are various renewable energy facilities and power generation data, system operation data, metering and rate-related data, as well as weather and energy efficiency data necessary for new services and analysis. Energy big data processing technology can systematically analyze and diagnose data generated in the first half of the power production and consumption infrastructure, including distributed resources, systems, and AMI. Through this, it will be a technology that supports the creation of new businesses in convergence between the ICT industry and the energy industry. To this end, research on the data analysis system, such as itemized characteristic analysis of the collected data, correlation sampling, categorization of each feature, and element definition, is needed. In addition, research on data purification technology for data loss and abnormal state processing should be conducted. In addition, it is necessary to develop and structure NIFI, Spark, and HDFS systems so that energy data can be stored and managed in real time. In this study, the overall energy data processing technology and system for various power transactions as described above were proposed.