• Proceedings of the KIPE Conference
• /
• 2007.07a
• /
• pp.412-414
• /
• 2007

In motor driving, one of the most important consideration is the load characteristic and variation. Generally, the motor drive should be made enough for the current by load variation, and it should be controlled by the load weight. However, the drum washing machine's load variation is irregular and large. Therefore, we want to make the motor drive that considering this load pattern, this paper describes the drum washing machine's load pattern modeling by the mathematical theory.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.54 no.4
• /
• pp.185-192
• /
• 2005

This paper proposes a DRMS(Distribution Remote Management System) which can enhance highly the economics of automatic metering system and the power quality supplied to the electric customer improving the efficiency of the meter reading, voltage management and load management work by realizing the remote meter reading, the remote voltage management and the remote load management based on the ADWHM(Advanced Digital Watt Hour Meter). The DRMS is designed so that the voltage management and load management work in remote site can be processed by collecting the voltage pattern and current pattern as well as watt hour data from all ADWHMs one time every month regularly or from special ADWHMS several time irregularly, A new on-line voltage and load management strategy based on the ADWHM is designed by analyzing the existing voltage management and load management process. Also, DRMS is designed so that watt-hour data, voltage pattern data, load pattern data and power factor data can be collected selectively according to the selection of user to assist effectively the methodology. Remote management program and database of the DRMS are implemented based on Visual C++, MFC and database library of MS. Also, DRMS is designed so as to communicate with the ADWHM using RS232C-TCP/IP converter and ADSL. The effectiveness of the remote metering function is proven by collecting and analyzing the data after ADWHMs installed in any site. The developed strategy and program also is verified through the simulation of voltage management and load management.

• Journal of Korea Society of Industrial Information Systems
• /
• v.18 no.3
• /
• pp.35-46
• /
• 2013

In order to develop an accurate analytical model for domestic electricity demand forecasting, we propose a prediction method of the electric power demand pattern by combining SMO classification techniques and a dimension reduction conceptualized subspace clustering techniques suitable for high-dimensional data cluster analysis. In terms of electricity demand pattern prediction, hourly electricity load patterns and the demographic and geographic characteristics can be analyzed by integrating the wireless load monitoring data as well as sub-regional unit of census information. There are composed of a total of 18 characteristics clusters in the prediction result for the sub-regional demand pattern by using census information and power load of Seoul metropolitan area. The power demand pattern prediction accuracy was approximately 85%.

• Proceedings of the KIEE Conference
• /
• 1991.11a
• /
• pp.39-42
• /
• 1991

The Application of Artificial Neural Network(ANN) to forecast a load in a power system is investigated. The load forecasting is important in the electric utility industry. This technique, methodology based on the fact that parallel structure can process very fast much information is a promising approach to a load forecasting. ANN that is highly interconnected processing element in a hierachy activated by the each input. The load pattern can be divided distinctively into two patterns, that is, weekday and weekend. ANN is composed of a input layer, several hidden layers, and a output layer and the past data is used to activate input layer. The output of ANN is the load forecast for a given day. The result of this simulation can be used as a reference to a electric utility operation.

• Proceedings of the KIEE Conference
• /
• 2002.07a
• /
• pp.432-434
• /
• 2002

Recently, electricity demand of remote islands in Korea has been rapidly increased. It's mainly due to increase of income level resulted from economic development. Electricity demand patterns and characteristics in remote islands are different from those of mainland in point of time of peak load, demographic and industrial characteristics of islands, and so on. The optimal power supply in remote islands has a important relationship with accurate analysis of island's load characteristics, the adoption of relevant load forecasting technique, and optimal power facilities reflecting local's electricity demand characteristics. This paper shows the recent load pattern and characteristics, load forecasting using probability distribution, and the perpetration of relevant power facilities in remote islands.

• Journal of the Institute of Convergence Signal Processing
• /
• v.2 no.3
• /
• pp.73-78
• /
• 2001

BP neural network model and multiple-regression model were composed for forecasting the special-days load. Special-days load was forecasted using that neural network model made use of pattern conversion ratio and multiple-regression made use of weekday-change ratio. This methods identified the suitable as that special-days load of short and long term was forecasted with the weekly average percentage error of 1∼2[%] in the weekly peak load forecasting model using pattern conversion ratio. But this methods were hard with special-days load forecasting of summertime. therefore it was forecasted with the multiple-regression models. This models were used to the weekday-change ratio, and the temperature-humidity and discomfort-index as explanatory variable. This methods identified the suitable as that compared forecasting result of weekday load with forecasting result of special-days load because months average percentage error was alike. And, the fit of the presented forecast models using statistical tests had been proved. Big difficult problem of peak load forecasting had been solved that because identified the fit of the methods of special-days load forecasting in the paper presented.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.16 no.2
• /
• pp.98-103
• /
• 2002

In case of power demand forecasting, the most important problems are to deal with the load of special-days. Accordingly, this paper presents the method that forecasting long (the Lunar New Year, the Full Moon Festival) and short(the Planting Trees Day, the Memorial Day, etc) special-days peak load using neural networks and regression models. long and short special-days peak load forecast by neural networks models uses pattern conversion ratio and four-order orthogonal polynomials regression models. There are using that special-days peak load data during ten years(1985∼1994). In the result of special-days peak load forecasting, forecasting % error shows good results as about 1 ∼2[%] both neural networks models and four-order orthogonal polynomials regression models. Besides, from the result of analysis of adjusted coefficient of determination and F-test, the significance of the are convinced four-order orthogonal polynomials regression models. When the neural networks models are compared with the four-order orthogonal polynomials regression models at a view of the results of special-days peak load forecasting, the neural networks models which uses pattern conversion ratio are more effective on forecasting long special-days peak load. On the other hand, in case of forecasting short special-days peak load, both are valid.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.61 no.12
• /
• pp.1765-1773
• /
• 2012

The accuracy of load forecast is very important from the viewpoint of economical power system operation. In general, the weekdays' load demand pattern has the continuous time series characteristics. Therefore, the conventional methods expose stable performance for weekdays. In case of special days or weekends, the load demand pattern has the discontinuous time series characteristics, so forecasting error is relatively high. Especially, weekdays near the thanksgiving day and lunar new year's day have the mixed load profile characteristics of both weekdays and weekends. Therefore, it is difficult to forecast these days by using the existing algorithms. In this study, a new load forecasting method is proposed in order to enhance the accuracy of the forecast result considering the characteristics of weekdays and weekends. The proposed method was tested with these days during last decades, which shows that the suggested method considerably improves the accuracy of the load forecast results.

• Journal of KIISE:Computing Practices and Letters
• /
• v.12 no.5
• /
• pp.312-325
• /
• 2006

Integration applications, such as EAI, B2Bi, need stable massive data processing systems during overload state cause by service request congestion in a short period time. In this paper, we propose the PLC (Peak Load Control)-based Worker-Linker pattern, which can effectively and stably process massive I/O transactions in spite of overload state generated by service request congestion. This pattern uses the delay time algorithm for the PLC mechanism. In this paper, we also show the example of applying the pattern to business-business integration framework and the experimental result for proving the stability of performance. According to our experiment result, the proposed delay time algorithm can stably control the heavy overload after the saturation point and has an effect on the controlling peak load.

• Journal of Dental Rehabilitation and Applied Science
• /
• v.23 no.1
• /
• pp.55-68
• /
• 2007

The purpose of this study was to analyze the distribution of stress in the surrounding bone around implant placed in the first and second molar region. Two different three-dimensional finite element model were designed according to vertical bone level around fixture ($4.0mm{\times}11.5mm$) on the second molar region. A mandibular segment containing two implant-abutments and a two-unit bridge system was molded as a cancellous core surrounded by a 2mm cortical layer. The mesial and distal section planes of the model were not covered by cortical bone and were constrained in all directions at the nodes. Two vertical loads and oblique loads of 200 N were applied at the center of occlusal surface (load A) or at a position of 2mm apart buccally from the center (load B). Von-Mises stresses were analyzed in the supporting bone. The results were as follows; 1. With the vertical load at the center of occlusal surface, the stress pattern on the cortical and cancellous bones around the implant on model 1 and 2 was changed, while the stress pattern on the cancellous bone with oblique load was not. 2. With the vertical load at the center of occlusal surface, the maximum von-Mises stress appeared in the outer distal side of the cortical bone on Model 1 and 2, while the maximum von-Mises stress appeared in the distal and lingual distal side of the cortical bone with oblique load. 3. With the vertical load at a position of 2 mm apart buccally from the center, there was the distribution of stress on the upper portion of the implant-bone interface and the cortical bone except for the cancellous bone, while there was a distribution of stress on the cancellous bones at the apical and lingual sides around the fixture and on the cortical bone with oblique load. 4. With the changes of the supporting bone on the second molar area, the stress pattern on the upper part of the cortical bone between two implants was changed, while the stress pattern on the cancellous bone was not. The results of this study suggest that establishing the optimum occlusal contact considering the direction and position of the load from the standpoint of stress distribution of surrounding bone will be clinically useful.