• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.590-591
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• 2011

This paper proposes a short-term load forecasting by using a temperature and load pattern. The forecasting model that represents the relations between load and temperature which get a numeral expected temperature based on the past temperature was constructed. Case studies were applied to load forecasting for 2009 data, and the results show its appropriate accuracy.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.594-597
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• 2007

We present in this paper a novel mid and long term power load prediction method using temporal pattern mining from AMR (Automatic Meter Reading) data. Since the power load patterns have time-varying characteristic and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Also, research on data mining for analyzing electric load patterns focused on cluster analysis and classification methods. However despite the usefulness of rules that include temporal dimension and the fact that the AMR data has temporal attribute, the above methods were limited in static pattern extraction and did not consider temporal attributes. Therefore, we propose a new classification method for predicting power load patterns. The main tasks include clustering method and temporal classification method. Cluster analysis is used to create load pattern classes and the representative load profiles for each class. Next, the classification method uses representative load profiles to build a classifier able to assign different load patterns to the existing classes. The proposed classification method is the Calendar-based temporal mining and it discovers electric load patterns in multiple time granularities. Lastly, we show that the proposed method used AMR data and discovered more interest patterns.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.36 no.4
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• pp.369-375
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• 2023

In this paper, we compared and analyzed the power load patterns of dormitory buildings and office buildings to use them as basic data (demand analysis and capacity design) for the design and operation of microgrids for multi-use facilities, and the following conclusions were got. During the daytime on regular weekdays, the power consumption load pattern of office buildings was relatively large at 264.0~332.3 kWh, and during the evening hours, the power consumption load pattern of dormitory buildings was relatively large at 233.0~258.3 kWh. In the case of vacation, during the daytime on weekdays, the power consumption load pattern of office buildings was relatively large at 279.1~407.4 kWh, and in the evening, the power consumption load pattern of dormitory buildings was relatively high at 280.1~394.1 kWh. During the daytime on regular weekends, the power consumption of dormitory-type buildings was relatively high at 133.5~201.6 kWh, and it was found that the power consumption of dormitory-type buildings appeared relatively high at 187.5~252.1 kWh. During a vacation in the daytime on weekends, the power consumption of dormitory-type buildings was found to be 186.5 kWh~ and 218.6 kWh. The increase in power consumption during a vacation (December-February) compared to normal (April-June) was thought to be due to an increase in electricity demand, and the reason for the higher power consumption in dormitory buildings during the vacation was due to reduced working hours in office buildings.

• Korean Journal of Remote Sensing
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• v.23 no.5
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• pp.393-400
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• 2007

We present in this paper a novel power load prediction method using temporal pattern mining from AMR(Automatic Meter Reading) data. Since the power load patterns have time-varying characteristic and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Also, research on data mining for analyzing electric load patterns focused on cluster analysis and classification methods. However despite the usefulness of rules that include temporal dimension and the fact that the AMR data has temporal attribute, the above methods were limited in static pattern extraction and did not consider temporal attributes. Therefore, we propose a new classification method for predicting power load patterns. The main tasks include clustering method and temporal classification method. Cluster analysis is used to create load pattern classes and the representative load profiles for each class. Next, the classification method uses representative load profiles to build a classifier able to assign different load patterns to the existing classes. The proposed classification method is the Calendar-based temporal mining and it discovers electric load patterns in multiple time granularities. Lastly, we show that the proposed method used AMR data and discovered more interest patterns.

• Steel and Composite Structures
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• v.32 no.1
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• pp.67-77
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• 2019

Pushover analysis captures the behavior of a structure from fully elastic to collapse. In this analysis, the structure is subjected to increasing lateral load with constant gravity one. Neglecting the effects of the higher modes and the changes in the vibration characteristics during the nonlinear analysis are the main obstacles of the proposed lateral load patterns. To overcome these drawbacks, whereas some methods have been presented to achieve updated lateral load distribution, these methods are not precisely capable to predict the response of structures, precisely. In this study, a new method based on optimization procedure is developed to obtain a lateral load pattern for which the difference between the floor displacements of pushover and Nonlinear Dynamic Analyses (NDA) is minimal. For this purpose, an optimization problem is considered and the genetic algorithm is applied to calculate optimal lateral load pattern. Three special moment resisting steel frames with different dynamic characteristics are simulated and their optimal load patterns are derived. The floor displacements of these frames subjected to the proposed and conventional load patterns are acquired and the accuracy of them is evaluated via comparing with NDA responses. The outcomes reveal that the proposed lateral load distribution is more accurate than the previous ones.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.10
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• pp.410-417
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• 2006

Generally, the training set of maximum $m{\times}L(m+f)$ patterns in the pattern recognition method is required for the real-time bus reconfiguration strategy when a main transformer fault occurs in the distribution substation. Accordingly, to make the application of pattern recognition method possible, the size of the training set must be reduced as efficient level. This Paper proposes a methodology which obtains the minimized training set by applying the pattern clustering method to load patterns of the main transformers and feeders during selected period and to obtain bus reconfiguration strategy based on it. The MaxMin distance clustering algorithm is adopted as the pattern clustering method. The proposed method reduces greatly the number of load patterns to be trained and obtain the satisfactory pattern matching success rate because that it generates the typical pattern clusters by appling the pattern clustering method to load patterns of the main transformers and feeders during selected period. The proposed strategy is designed and implemented in Visual C++ MFC. Finally, availability and accuracy of the proposed methodology and the design is verified from diversity simulation reviews for typical distribution substation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2215-2220
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• 2011

The accurate short-term load forecasting is essential for the efficient power system operation and the system marginal price decision of the electricity market. So far, errors of load forecasting for Chuseok Holiday are very big compared with forecasting errors for the other special days. In order to improve the accuracy of load forecasting for Chuseok Holiday, selection of input data, the daily normalized load patterns and load forecasting model are investigated. The efficient data selection and daily normalized load pattern based on fuzzy linear regression model is proposed. The proposed load forecasting method for Chuseok Holiday is tested in recent 5 years from 2006 to 2010, and improved the accuracy of the load forecasting compared with the former research.

• The Transactions of the Korean Institute of Power Electronics
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• v.12 no.6
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• pp.491-499
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• 2007

The variation of a load characteristic in the motor drive is one of the most important consideration. Because the current flowing into the motor generally varies according to the load variation, it needs to design the motor drive circuit properly in accordance with the load variation. However, the load variation of the drum washing machine is irregular and large due to the water flow and reverse load torque. Therefore, to design the motor drive circuit considering this load pattern, simulation results shows the load pattern modeling of the drum washing machine based on the physical analysis in this paper.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.450-453
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• 2003

This paper presents the estimation on Load Characteristic Factor(k) which is considered to load pattern and seasonal characteristic of consumer. We can calculate the loss of distribution networks through the equation composing of Load Factor(LF), Loss Load Factor(LLF) and load characteristic factor(k). This equation is similar to the method of Regulator-General Victoria, Australia. Generally, the conventional method for calculating the distribution losses uses k with a constant value from 0.1 to 0.3. However, the k which is a relationship between LF and LLF can be varied by load pattern and seasonal characteristics. It is necessary to estimate the k according to load characteristics. This paper shows the result for recalculating k using the KEPCO's SOMAS data measured in distribution networks.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.28-34
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• 2009

In this study firstly we survey the calculation method and the characteristics of the way of estimating CBL(Customer BaseLine Load) that is important calculation tool for DRP internationally. Also we analyze the power consumption pattern using the 15 minutes load profiles of about 120,000 customers in domestic. Based on this pattern, we provide the CBL calculation method that can be utilized in DRP to save the cost, and analyze the accuracy of the CBL calculation proposed in this paper through the simulation.