• 품질경영학회지
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• 제43권4호
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• pp.573-588
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• 2015

Purpose: For improving result of estimated remaining useful life in Prognostics and Health Management (PHM), a system which is able to consider a lot of environment and load data is required. Method: A load profile monitoring system was presented based on cloud computing for gathering and processing raw data which is included environment and load data. Result: Users can access results of load profile information on the Internet. The developed system provides information which consists of distribution of load data, basic statistics, etc. Conclusion: We developed the load profile monitoring system for considering much environment and load data. This system has advantages such as improving accessibility through smart device, reducing cost, and covering various conditions.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 A
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• pp.683-685
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• 2004

This paper introduces a basic design for the short-term load forecasting system using a commercial data base. The proposed system uses a hybrid load forecasting method using fuzzy linear regression for forecasting of weekends and Monday and general exponential smoothing for forecasting of weekdays. The temperature sensitive is used to improve the accuracy of the load forecasting during the summer season. MS-SQL Sever has been used a commercial data base for the proposed system and the database is operated by ADO(ActiveX Data Objects) and RDO(Remote Data Object). Database has been constructed by altering the historical load data for the past 38 years. The weather iDormation is included in the database. The developed short-term load forecasting system is developed as a user friendly system based on GUI(Graphical User interface) using MFC(Microsoft Foundation Class). Test results show that the developed system efficiently performs short-term load forecasting.

• 대한전기학회논문지:전력기술부문A
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• 제48권1호
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• pp.8-14
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• 1999

This paper presents the methods of short-term load forecasting Kohonen neural networks and back-propagation neural networks. First, historical load data is divided into 5 patterns for the each seasonal data using Kohonen neural networks and using these results, load forecasting neural network is used for next day hourly load forecasting. Next day hourly load of weekdays and weekend except holidays are forecasted. For load forecasting in summer, max-temperature and min-temperature data as well as historical hourly load date are used as inputs of load forecasting neural networks for a better forecasting accuracy. To show the possibility of the proposed method, it was tested with hourly load data of Korea Electric Power Corporation(1994-95).

• International Journal of Air-Conditioning and Refrigeration
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• 제11권4호
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• pp.170-177
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• 2003

The electric power load during the summer peak time is strongly affected by cooling load, which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice storage system and heat pump system etc. are used to settle this problem. In this study, the method of estimating temperature and humidity to forecast the cooling load of ice storage system is suggested. The method of forecasting the cooling load using neural network is also suggested. The daily cooling load is mainly dependent on actual temperature and humidity of the day. The simulation is started with forecasting the temperature and humidity of the following day from the past data. The cooling load is then simulated by using the forecasted temperature and humidity data obtained from the simulation. It was observed that the forecasted data were closely approached to the actual data.

• 한국멀티미디어학회논문지
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• 제23권2호
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• pp.247-254
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• 2020

The recent Internet environment is characterized by the explosion of certain types of data, as the data that people want is affected by certain issues. In this paper, we propose a load balancing technique that considers the data generation forms. The concept of this technique is to prioritize some type of data when it suddenly explodes. This is a technique to build an add-on middle box on a switch to monitor packets and give priority to a queue for load balancing. This technique worked when certain types of data exploded. SDN(Software Defined Networking) has the advantage of efficiently managing a number of network equipment. However, load balancing in the SDN environment has not been studied much. Applying the proposed load balancing technique in the SDN environment can save time and budget and easily implement our policies. When the proposed load balancing technique is applied to the SDN environment, it has been found that the techniques we want can be easily applied to the network systems, and that efficient data processing is possible when certain types of data explosion.

• 대한전기학회논문지:전력기술부문A
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• 제48권6호
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• pp.699-703
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• 1999

It is known that the LDC(Line-Drop Compensator) becomes to lose the function of proper voltage regulation for its load currents due to the real and reactive power generated by DGS(Dispersed Generation System), when DGS is introduced into the power distribution system of which the voltage is controlled by LDC. Therefore, in that case, it is very difficult to regulate the distribution line voltage properly by using LDC. One possible solution for this problem is the real-time voltage regulation method which is to optimally regulate the sending-end voltage in real-time by collecting the real-time load data of each load data of each load section between measuring points and by calculating the optimal seding-end voltage value from them. For this, we must know the real-time load data of each load section. In this paper, a modeling method of representing a load section on high voltage line with DGSs as an equivalent lumped load is proposed for gaining the real-time load data. In addition a method of locating the measuring points is proposed. Then, these proposed methods are evaluated through computer simulations.

• Smart Structures and Systems
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• 제30권2호
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• pp.145-157
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• 2022

The design of safe and economical structures depends on the reliable live load from load survey. Live load surveys are traditionally conducted by randomly selecting rooms and weighing each item on-site, a method that has problems of low efficiency, high cost, and long cycle time. This paper proposes a deep learning-based method combined with Internet big data to perform live load surveys. The proposed survey method utilizes multi-source heterogeneous data, such as images, voice, and product identification, to obtain the live load without weighing each item through object detection, web crawler, and speech recognition. The indoor objects and face detection models are first developed based on fine-tuning the YOLOv3 algorithm to detect target objects and obtain the number of people in a room, respectively. Each detection model is evaluated using the independent testing set. Then web crawler frameworks with keyword and image retrieval are established to extract the weight information of detected objects from Internet big data. The live load in a room is derived by combining the weight and number of items and people. To verify the feasibility of the proposed survey method, a live load survey is carried out for a meeting room. The results show that, compared with the traditional method of sampling and weighing, the proposed method could perform efficient and convenient live load surveys and represents a new load research paradigm.

• 대한원격탐사학회지
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• 제23권5호
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.199-200
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• 2011

Load forecasting is very important for power system analysis and planning. This paper suggests yearly load forecasting of considering weekly normalization and seasonal load characteristics. Each weekly peak load is normalized and the average value is calculated. The new hourly peak load is seasonally collected. This method was used for yearly load forecasting. The results of the actual data and forecast data were calculated error rate by comparing.

• 한국태양에너지학회 논문집
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• 제29권3호
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• pp.66-72
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• 2009

As 'Low Carbon Green Building' is highly required, programs to evaluate building performance are actively and commonly used. For most of these programs, dynamic responses of buildings against external weather changes are very important. In order to simulate the programs, weather data of each region must be properly entered to estimate accurate amount of building energy consumption. To this end, the existing weather data and weather data of KSES were compared and analyzed to find out how weather changes. Energy load of Korea's standard houses was also analyzed based on this data. As a result, data corresponding to June ${\sim}$ September when cooling is supplied shows 23% of average increase with 30% of peak increase(June). On the other hand, data corresponding to November ${\sim}$ February when heating is supplied shows 29% of average decrease with 34% of peak decrease(November). Increase in cooling load and decrease in heating load in the above data comparison/analysis show that KSES 2009 data reflects increase in average temperature caused by global warming unlike the existing data. Increase in dry-bulb temperature depending on weather change of standard houses increases cooling load by 17% and decreases heating load by 36%