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  • Title/Summary/Keyword: Power Load Forecasting

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Short-Term Load Forecast for Near Consecutive Holidays Having The Mixed Load Profile Characteristics of Weekdays and Weekends (평일과 주말의 특성이 결합된 연휴전 평일에 대한 단기 전력수요예측)

  • Park, Jeong-Do;Song, Kyung-Bin;Lim, Hyeong-Woo;Park, Hae-Soo
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.61 no.12
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    • pp.1765-1773
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    • 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.

Daily Load Forecasting Including Special Days Using Hourly Relative factors (시간대별 상대계수를 이용한 특수일이 포함된 평일의 전력수요예측)

  • Ahn, Dae-Hoon;Lee, Sang-Joong
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.19 no.5
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    • pp.94-102
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    • 2005
  • This paper performs analysis the load patterns for the all the special days and studies the change of the load patterns for the last 15 years using Expert system based on the load record and the weather condition record. The Expert system is one of the four major load forecasting methods of the power system And it is used for forecasting. loads of the special days based on the Know-how of the load forecasting Experts. After the author simulates the load forecasting using hourly relative factors of the load patterns based on the past load records, there is considerable improved effect. The average errors of past 5 days load forecasting of lunar New Year's Day (year 2002 and 2003) is 3.23[%]. Using the new method the author forecast loads of the lunar new year's days (the year 2005) and it shows only 1.78[%] error. A field manual for the load forecast can be made using proposed method. The authors expect this article could give a guidance to those who wish to be load forecast expert.

Various Models of Fuzzy Least-Squares Linear Regression for Load Forecasting (전력수요예측을 위한 다양한 퍼지 최소자승 선형회귀 모델)

  • Song, Kyung-Bin
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.21 no.7
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    • pp.61-67
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    • 2007
  • The load forecasting has been an important part of power system Accordingly, it has been proposed various methods for the load forecasting. The load patterns of the special days is quite different than those of ordinary weekdays. It is difficult to accurately forecast the load of special days due to the insufficiency of the load patterns compared with ordinary weekdays, so we have proposed fuzzy least squares linear regression algorithm for the load forecasting. In this paper we proposed four models for fuzzy least squares linear regression. It is separated by coefficients of fuzzy least squares linear regression equation. we compared model of H1 with H4 and prove it H4 has accurately forecast better than H1.

Daily Electric Load Forecasting Based on RBF Neural Network Models

  • Hwang, Heesoo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.13 no.1
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    • pp.39-49
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    • 2013
  • This paper presents a method of improving the performance of a day-ahead 24-h load curve and peak load forecasting. The next-day load curve is forecasted using radial basis function (RBF) neural network models built using the best design parameters. To improve the forecasting accuracy, the load curve forecasted using the RBF network models is corrected by the weighted sum of both the error of the current prediction and the change in the errors between the current and the previous prediction. The optimal weights (called "gains" in the error correction) are identified by differential evolution. The peak load forecasted by the RBF network models is also corrected by combining the load curve outputs of the RBF models by linear addition with 24 coefficients. The optimal coefficients for reducing both the forecasting mean absolute percent error (MAPE) and the sum of errors are also identified using differential evolution. The proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange. Simulation results reveal satisfactory forecasts: 1.230% MAPE for daily peak load and 1.128% MAPE for daily load curve.

Distribution Load Forecasting based with Land-use Estimation (토지용도 추정을 기반으로 한 배전계통 부하예측)

  • Kwon, Seong-Chul;Lee, Hak-Joo;Choi, Byoung-Youn
    • Proceedings of the KIEE Conference
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    • 1999.07c
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    • pp.1481-1483
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    • 1999
  • Power distribution system planning for maximum customer satisfaction and system efficiency requires accurate forecast of future demand in service area. Spatial load forecasting method provides a more accurate estimation of both magnitudes and location of future electrical load. This method considers the causes of load growth due to addition of customers and per capita consumption among customers by land use (residential, commercial and industrial). So the land-use study and it's preference for small area is quite important. This paper proposes land-use preference estimation method based on fuzzy logic. Fuzzy logic is applied to computing preference scores for each land-use and by these scores the customer growth is allocated in service area. An simulation example is used to illustrate the proposed method.

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A Daily Maximum Load Forecasting System Using Chaotic Time Series (Chaos를 이용한 단기부하예측)

  • Choi, Jae-Gyun;Park, Jong-Keun;Kim, Kwang-Ho
    • Proceedings of the KIEE Conference
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    • 1995.07b
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    • pp.578-580
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    • 1995
  • In this paper, a method for the daily maximum load forecasting which uses a chaotic time series in power system and artificial neural network. We find the characteristics of chaos in power load curve and then determine a optimal embedding dimension and delay time, For the load forecast of one day ahead daily maximum power, we use the time series load data obtained in previous year. By using of embedding dimension and delay time, we construct a strange attractor in pseudo phase plane and the artificial neural network model trained with the attractor font mentioned above. The one day ahead forecast errors are about 1.4% of absolute percentage average error.

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A short-term Load Forecasting Using Chaotic Time Series (Chaos특성을 이용한 단기부하예측)

  • Choi, Jae-Gyun;Park, Jong-Keun;Kim, Kwang-Ho
    • Proceedings of the KIEE Conference
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    • 1996.07b
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    • pp.835-837
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    • 1996
  • In this paper, a method for the daily maximum load forecasting which uses a chaotic time series in power system and artificial neural network(Back-propagation) is proposed. We find the characteristics of chaos in power load curve and then determine a optimal embedding dimension and delay time. For the load forecast of one day ahead daily maximum power, we use the time series load data obtained in previous year. By using of embedding dimension and delay time, we construct a strange attractor in pseudo phase plane and the artificial neural network model trained with the attractor mentioned above. The one day ahead forecast errors are about 1.4% for absolute percentage average error.

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Special-Days Load Handling Method using Neural Networks and Regression Models (신경회로망과 회귀모형을 이용한 특수일 부하 처리 기법)

  • 고희석;이세훈;이충식
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.16 no.2
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    • pp.98-103
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    • 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 Study on Cooling Load Forecast of an Unit Building using Neural Networks

  • Shin, Kwan-Woo;Lee, Youn-Seop
    • International Journal of Air-Conditioning and Refrigeration
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    • v.11 no.4
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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.

Working Electrical Energy Forecasting for Peak Load Estimation of Distribution Transformer (주상변압기 최대부하 추정을 위한 수용가 사용전력량 예측)

  • Park, Chang-Ho;Cho, Seong-Soo;Kim, Jae-Cheol;Kim, Du-Bong;Yun, Sang-Yun;Lee, Dong-Jun
    • Proceedings of the KIEE Conference
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    • 1998.07c
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    • pp.929-931
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    • 1998
  • This paper describes the peak load forecasting technique of distribution transformers with correlation equation. While customers are demanding safe energy supply, conventional correlation equation that is used for load management of distribution transformers in domestic has some problems. To get accurate correlation equation, se-correlation equation were examined using new collected using the measuring instrument dev for this study. It was recognized that the qua equation was the most accurate for peak forecasting from working electrical energy.

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