• Proceedings of the KIEE Conference
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• summer
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• pp.722-724
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• 2004

Demand estimates in electric power systems have traditionally consisted of time-series analyses over long time periods. The resulting database consisted of huge amounts of data that were then analyzed to create the various coefficients used to forecast power demand. In this research, we take advantage of universally used analysis techniques analysis, but we also use easily available data-mining techniques to analyze patterns of days and special days(holidays, etc.). We then present a new method for estimating and forecasting power flow using decision tree analysis. And because analyzing the relationship between the estimate and power system ceiling Trices currently set by the Korea Power Exchange. We included power system ceiling prices in our estimate coefficients and estimate method.

• Journal of Industrial Technology
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• v.15
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• pp.187-193
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• 1995

In this paper, a short-term load forecasting expert system considering the load variation due to the change in temperature is presented. The change in temperature is an important load variation factor that varies the normal load pattern. The conventional load forecasting methods by artificial neural networks have used the technique where the temperature variables were included in the input neurons of artificial neural networks. However, simply adding the input units of temperature data may make the forecasting accuracy worse, since the accuracy of the load forecasting in this method depends on the accuracy of weather forecasting. In this paper, the fuzzy expert system that modifies the forecasted load using fuzzy rules representing the relations of load and temperature is presented and compared with a conventional load forecasting technique. In the test case of 1991, the proposed model provided a more accurate forecast than the conventional technique.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.91-93
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• 2004

Load forecasting is essential in order to supply electrical energy stably and economically in power systems. ANNs have flexibility to predict a nonlinear feature of load profiles. In this paper, we selected just the necessary input variables used in the paper(2) which is based on the phase-space embedding of a load time-series and reviewing others. So only 5 input variables were selected to forecast for spring, fall and winter season and another input considering temperature sensitivity is added during the summer season. The training cases are also selected from all previous data composed training cases of a 7-day, 14-day and 30-day period. Finally, we selected the training case of a 7-day period because it can be used in STLF without sacrificing the accuracy of the forecast. This allows more compact ANNs, smaller training cases. Consequently, test results show that compact neural networks can be forecasted without sacrificing the accuracy.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.6
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• pp.17-22
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• 2013

Fuzzy linear regression method has been used for short-term load forecasting of the special day in the previous researches. However, considerable load forecasting errors would be occurring if a special day is located on Saturday or Monday. In this paper, a new load forecasting method for the consecutive holidays is proposed with the consideration of the power demand variation rate. In the proposed method, a exponential smoothing model reflecting temperature is used to short-term load forecasting for Sunday during the consecutive holidays and then the loads of the special day during the consecutive holidays is calculated using the hourly power demand variation rate between the previous similar consecutive holidays. The proposed method is tested with 10 cases of the consecutive holidays from 2009 to 2012. Test results show that the average accuracy of the proposed method is improved about 2.96% by comparison with the fuzzy linear regression method.

• The Journal of Society for e-Business Studies
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• v.24 no.4
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• pp.119-133
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• 2019

Recently, the importance prediction of photovoltaic power (PV) is considered as an essential function for scheduling adjustments, deciding on storage size, and overall planning for stable operation of PV facility systems. In particular, since most of PV power is generated in peak time, PV power prediction in a peak time is required for the PV system operators that enable to maximize revenue and sustainable electricity quantity. Moreover, Prediction of the PV power output in peak time without meteorological information such as solar radiation, cloudiness, the temperature is considered a challenging problem because it has limitations that the PV power was predicted by using predicted uncertain meteorological information in a wide range of areas in previous studies. Therefore, this paper proposes the LSTM (Long-Short Term Memory) based the PV power prediction model only using the meteorological, seasonal, and the before the obtained PV power before peak time. In this paper, the experiment results based on the proposed model using the real-world data shows the superior performance, which showed a positive impact on improving the PV power in a peak time forecast performance targeted in this study.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.6
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• pp.39-43
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• 2013

The current representative temperature selection method with five cities cannot reflect the sufficient regional climate characteristics. In this paper, the new representative temperature selection method is proposed with the consideration of eight representative cities. The proposed method considered the recent trend of power sales, the climate characteristics and population distribution to improve the accuracy of short-term load forecasting. Case study results for the accuracy of short-term load forecasting are compared for the traditional temperature weights of five cities and the proposed temperature weights of eight cities. The simulation results show that the proposed method provides more accurate results than the traditional method.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.292-294
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• 2000

This paper is presented the method peak load forecast based on multiple regression Model. Forecasting model was composed with the temperature-humidity and the discomfort index. Also the week periodicity was excluded from weekday change coefficient of two types. Forecasting result was good with about 3[%]. And, utility of presented forecast model using statistical tests has been proved. Therefore, This results establish appropriateness and fitness of forecast models using peak power demand forecasting.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.295-297
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• 2000

In case of power demand forecasting the most important problem is to deal with the load of special-days, Accordingly, this paper presents a method that forecasting special-days load with regression models and neural networks. Special-days load in summer season was forecasted by the multiple regression models using weekday change ratio Neural networks models uses pattern conversion ratio, and orthogonal polynomial models was directly forecasted using past special-days load data. forecasting result obtains % forecast error of about $1{\sim}2[%]$. Therefore, it is possible to forecast long and short special-days load.

• Proceedings of the KIEE Conference
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• summer
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.12
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• pp.1657-1660
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• 2013

Short-term load forecasting for Chusok and New Year's consecutive holidays is very difficult, due to the irregular characteristics compared with ordinary weekdays and insufficient holidays historical data. During consecutive holidays of New Year and Chusok, most of industries reduce their operation rates and their electrical load levels. The correlation between businesses' operation rates and their loads during consecutive holidays of New Year and Chusok is analysed and short-term load forecasting algorithm for consecutive holidays considering businesses' operation rates of industries is proposed. Test results show that the proposed method improves the accuracy of short-term load forecasting over fuzzy linear regression method.