• Journal of Electrical Engineering and Technology
• /
• v.7 no.6
• /
• pp.807-813
• /
• 2012

In this paper, we introduce data mining techniques for short-term load forecasting (STLF). First, we use the K-mean algorithm to classify historical load data by season into four patterns. Second, we use the k-NN algorithm to divide the classified data into four patterns for Mondays, other weekdays, Saturdays, and Sundays. The classified data are used to develop a time series forecasting model. We then forecast the hourly load on weekdays and weekends, excluding special holidays. The historical load data are used as inputs for load forecasting. We compare our results with the KEPCO hourly record for 2008 and conclude that our approach is effective.

• Journal of IKEEE
• /
• v.22 no.3
• /
• pp.672-679
• /
• 2018

Recently, microgrid (MG) has been proposed as one of the most critical solutions for various energy problems. For the optimal and economic operation of MGs, it is very important to forecast the load profile. However, it is not easy to predict the load accurately since the load in a MG is small and highly variable. In this paper, we propose an artificial neural network (ANN) based method to predict the energy use in campus buildings in short-term time series from one hour up to one week. The proposed method analyzes and extracts the features from the historical data of load and temperature to generate the prediction of future energy consumption in the building based on sparsified K-means. To evaluate the performance of the proposed approach, historical load data in hourly resolution collected from the campus buildings were used. The experimental results show that the proposed approach outperforms the conventional forecasting methods.

• Journal of Powder Materials
• /
• v.17 no.3
• /
• pp.242-250
• /
• 2010

In this research, the optimal manufacturing conditions of fine Si powders from Si scrap were investigated as a function of different initial powder size using the high-energy ball milling equipment, which produces the fine powder by means of an ultra high-energy within a short duration. The morphological change of the powders according to the milling time was observed by Scanning electron microscopy (SEM). With the increasing milling time, the size of Si powder was decreased. In addition, more energy and stress for milling were required with the decreasing initial powder size. The refinement of Si scrap was rapidly carried out at 10min ball milling time. However, the refined powder started to agglomerate at 30 min milling time, while the powder size became uniform at 60 min milling time.

• 한국태양에너지학회:학술대회논문집
• /
• 2012.03a
• /
• pp.134-139
• /
• 2012

This study observed downward long and short-wave radiant environment with selecting 4 areas which have different height in downtown and 1 suburban area to figure out the characteristic of radiant environment in each altitude. The purpose of this study is to collect the preliminary data for interpreting urban thermal environment in summer season by analyzing thermal characteristic of atmosphere in the upper of downtown. The results of this study are as follows. 1) The higher altitude has the lower temperature, and temperature difference was more huge in day time than night time. 2) The short wave radiation according to altitude was higher as altitude was high. 3) Generally, the higher altitude has the lower air temperature, and also the higher altitude has the lower downward long wave radiation by the atmospheric radiation. 4) The ratio short wave radiation of long wave radiation was lower as altitude was high. And the urbanization effect was higher as the ratio was low.

• Journal of the Korea Society of Computer and Information
• /
• v.20 no.9
• /
• pp.61-66
• /
• 2015

We investigate rate scheduling and power allocation problem for a delay constrained CDMA systems. Specifically, we determine an energy efficient scheduling policy, while each user maintains the short term (n time slots) average throughput. More importantly, it is shown that the optimal transmission strategy for the uplink is same as that of the downlink, called uplink and downlink duality. We then examine the performance of the optimum transmission strategy for the uplink and the downlink for various system environments.

• Communications for Statistical Applications and Methods
• /
• v.22 no.6
• /
• pp.675-683
• /
• 2015

In the 21st century, we now face the serious problems of the enormous consumption of the energy resources. Depending on the power consumption increases, both China and South Korea face a reduction in available resources. This paper considers the regression models and time-series models to compare the performance of the forecasting accuracy based on Mean Absolute Percentage Error (MAPE) in order to forecast the electricity demand accurately on the short-term period (68 months) data in Northeast China and find the relationship with Korea. Among the models the support vector regression (SVR) model shows superior performance than time-series models for the short-term period data and the time-series models show similar results with the SVR model when we use long-term period data.

• Journal of Advanced Navigation Technology
• /
• v.23 no.6
• /
• pp.561-569
• /
• 2019

Solar photovoltaic can provide electrical energy with only radiation, and its use is expanding rapidly as a new energy source. This study predicts the short and long-term PV power generation using actual converter output data of photovoltaic system. The prediction algorithm uses multiple linear regression, support vector machine (SVM), and deep learning such as deep neural network (DNN) and long short-term memory (LSTM). In addition, three models are used according to the input and output structure of the weather element. Long-term forecasts are made monthly, seasonally and annually, and short-term forecasts are made for 7 days. As a result, the deep learning network is better in prediction accuracy than multiple linear regression and SVM. In addition, LSTM, which is a better model for time series prediction than DNN, is somewhat superior in terms of prediction accuracy. The experiment results according to the input and output structure appear Model 2 has less error than Model 1, and Model 3 has less error than Model 2.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.41 no.8
• /
• pp.831-840
• /
• 1992

This paper presents an efficient short-term generation scheduling method using a rule-based expert/consulting system approach to assist electric energy system operators and planners. The expert system approach is applied to improve the Dynamic Programming(DP) based generation scheduling algorithm. In the selection procedure of the feasible combinations of generating units at each stage, automatic consulting on the manipulation of several constraints such as the minimum up time, the minimum down time and the maximum running time constraints of generating units will be performed by the expert/consulting system. In order to maximize the solution feasibility, the aforementioned constraints are controlled by a rule-based expert system, that is, instead of imposing penalty cost to those constraint violated combinations, which sometimes may become the very reason of no existing solution, several constraints will be manipulated within their flexibilities using the rules and facts that are established by domain experts. In this paper, for the purpose of implementing the consulting of several constraints during the dynamic process of generation scheduling, an expert system named STGSCS is developed. As a building tool of the expert system, C Language Integrated Production System(CLIPS) is used. The effectiveness of the proposed algorithm has been demonstrated by applying it to a model electric energy system.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2003.08a
• /
• pp.89-92
• /
• 2003

ITO thin films were deposited by RF-magnetron sputtering method and crystallization behavior of the films with and without external heating as a function of deposition time was examined. X-ray diffraction results indicated an amorphous state of the film when the deposition time is short about 10 min. When the deposition time was increased over 20 min development of crystallization of the films is observed. Because RF-sputtering transfers the high-energy to the growing film by energetic bombardment, it is believed that considerable activation energy for the crystallization of the film has transferred during deposition, which resulted in the crystallization of ITO thin films without external energy supply.

• Journal of IKEEE
• /
• v.22 no.4
• /
• pp.1210-1213
• /
• 2018

In this paper, we propose a method to reduce the amount of current through the Timing Control of the duty cycle and the Report Attribute Control at the MAC Layer in consideration of the Sleep Mode under the IoT wireless environment. The use of a duty cycle is an effective way to reduce energy consumption on wireless sensor networks where the node is placed in sleep mode periodically. In particular, we studied how to control power efficiency through duty rate in Short Transition Time and ACK Time processing while satisfying radio channel limitation criterion. When comparing before and after the improvement considering the delay time constraint, we validated the correlation of the electrical current reduction.