• Title/Summary/Keyword: Electric power load forecasting

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Weekly Maximum Electric Load Forecasting Method for 104 Weeks Using Multiple Regression Models (다중회귀모형을 이용한 104주 주 최대 전력수요예측)

  • Jung, Hyun-Woo;Kim, Si-Yeon;Song, Kyung-Bin
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.63 no.9
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    • pp.1186-1191
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    • 2014
  • Weekly and monthly electric load forecasting are essential for the generator maintenance plan and the systematic operation of the electric power reserve. This paper proposes the weekly maximum electric load forecasting model for 104 weeks with the multiple regression model. Input variables of the multiple regression model are temperatures and GDP that are highly correlated with electric loads. The weekly variable is added as input variable to improve the accuracy of electric load forecasting. Test results show that the proposed algorithm improves the accuracy of electric load forecasting over the seasonal autoregressive integrated moving average model. We expect that the proposed algorithm can contribute to the systematic operation of the power system by improving the accuracy of the electric load forecasting.

Very Short-term Electric Load Forecasting for Real-time Power System Operation

  • Jung, Hyun-Woo;Song, Kyung-Bin;Park, Jeong-Do;Park, Rae-Jun
    • Journal of Electrical Engineering and Technology
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    • v.13 no.4
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    • pp.1419-1424
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    • 2018
  • Very short-term electric load forecasting is essential for real-time power system operation. In this paper, a very short-term electric load forecasting technique applying the Kalman filter algorithm is proposed. In order to apply the Kalman filter algorithm to electric load forecasting, an electrical load forecasting algorithm is defined as an observation model and a state space model in a time domain. In addition, in order to precisely reflect the noise characteristics of the Kalman filter algorithm, the optimal error covariance matrixes Q and R are selected from several experiments. The proposed algorithm is expected to contribute to stable real-time power system operation by providing a precise electric load forecasting result in the next six hours.

Daily Maximum Electric Load Forecasting for the Next 4 Weeks for Power System Maintenance and Operation (전력계통 유지보수 및 운영을 위한 향후 4주의 일 최대 전력수요예측)

  • Jung, Hyun-Woo;Song, Kyung-Bin
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.63 no.11
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    • pp.1497-1502
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    • 2014
  • Electric load forecasting is essential for stable electric power supply, efficient operation and management of power systems, and safe operation of power generation systems. The results are utilized in generator preventive maintenance planning and the systemization of power reserve management. Development and improvement of electric load forecasting model is necessary for power system maintenance and operation. This paper proposes daily maximum electric load forecasting methods for the next 4 weeks with a seasonal autoregressive integrated moving average model and an exponential smoothing model. According to the results of forecasting of daily maximum electric load forecasting for the next 4 weeks of March, April, November 2010~2012 using the constructed forecasting models, the seasonal autoregressive integrated moving average model showed an average error rate of 6,66%, 5.26%, 3.61% respectively and the exponential smoothing model showed an average error rate of 3.82%, 4.07%, 3.59% respectively.

The 24 Hourly Load Forecasting of the Election Day Using the Load Variation Rate (부하변동율을 이용한 선거일의 24시간 수요예측)

  • Song, Kyung-Bin
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.59 no.6
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    • pp.1041-1045
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    • 2010
  • Short-term electric load forecasting of power systems is essential for the power system stability and the efficient power system operation. An accurate load forecasting scheme improves the power system security and saves some economic losses in power system operations. Due to scarcity of the historical same type of holiday load data, most big electric load forecasting errors occur on load forecasting for the holidays. The fuzzy linear regression model has showed good accuracy for the load forecasting of the holidays. However, it is not good enough to forecast the load of the election day. The concept of the load variation rate for the load forecasting of the election day is introduced. The proposed algorithm shows its good accuracy in that the average percentage error for the short-term 24 hourly loads forecasting of the election days is 2.27%. The accuracy of the proposed 24 hourly loads forecasting of the election days is compared with the fuzzy linear regression method. The proposed method gives much better forecasting accuracy with overall average error of 2.27%, which improved about average error of 2% as compared to the fuzzy linear regression method.

Design of Electric Power Load Forecasting Model based on IT2TSK FLS (IT2TSK 퍼지논리 기반 전력부하 예측 모델 설계에 관한 연구)

  • Bang, Young-Keun;Shim, Jae-Sun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.64 no.7
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    • pp.1088-1095
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    • 2015
  • In most cases, the use of electric power is associated with the economic scale of a nation closely. Thus, the electric power load forecasting plays an important role for the national economic plan. This paper deals with the design method for the electric power load forecasting system. In this paper, RCR-MA data processing, which can make the complex properties of the original data form simple, is proposed. Next, IT2TSK FLS, which can reflect the uncertainty of data more than T1TSK FLS, is applied. Consequently, the structural advantage of the proposed system can improve the forecasting accuracy, and is verified by using two types of electric power data.

Load Forecasting for Holidays Using a Fuzzy Least Squares Linear Regression Algorithm (퍼지 최소 자승 선형회귀분석 알고리즘을 이용한 특수일 전력수요예측)

  • Song Kyung-Bin;Ku Bon-Suk;Baek Young-Sik
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.52 no.4
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    • pp.233-237
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    • 2003
  • An accurate load forecasting is essential for economics and stability power system operation. Due to high relationship between the electric power load and the electric power price, the participants of the competitive power market are very interested in load forecasting. The percentage errors of load forecasting for holidays is relatively large. In order to improve the accuarcy of load forecasting for holidays, this paper proposed load forecasting method for holidays using a fuzzy least squares linear regression algorithm. The proposed algorithm is tested for load forecasting for holidays in 1996, 1997, and 2000. The test results show that the proposed algorithm is better than the algorithm using fuzzy linear regression.

Long-Term Load Forecasting in Metropolitan Area Considering Economic Indicator (대도시 지역의 경제지표를 고려한 장기전력 부하예측 기법)

  • Choe, Sang-Bong;Kim, Dae-Gyeong;Jeong, Seong-Hwan
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.49 no.8
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    • pp.380-389
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    • 2000
  • This paper presents a method for the regional long-term load forecasting in metropolitan area considering econimic indicator with the assumption that energy demands propoprtionally increases under the economic indicators. For the accurate load forecasting, it is very important to scrutinize the correlation among the regional electric power demands, economic indicator and other characteristics because load forecasting results may vary depending on many different factors such as electric power demands, gross products, social trend and so on. Three steps for the regional long-term load forecasting are microscopically and macroscopically used for the regional long -term load forecasting in order to increase the accuracy and practicality of the results.

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Short-term Electric Load Forecasting Using the Realtime Weather Information & Electric Power Pattern Analysis (실시간기상정보와 전력패턴을 이용한 단기 전력부하예측)

  • Kim, Il-Ju;Lee, Song-Keun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.65 no.6
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    • pp.934-939
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    • 2016
  • This paper made short-term electric load forecasting by using temperature data at three-hour intervals (9am, 12pm, 3pm, and 6pm) provided by the Korea Meteorological Administration (KMA). In addition, the electric power pattern was created using existing electric power data, and temperature sensitivity was derived using temperature and electric power data. We made power load forecasting program using LabVIEW, a graphic language.

Optimal Coefficient Selection of Exponential Smoothing Model in Short Term Load Forecasting on Weekdays (평일 단기전력수요 예측을 위한 최적의 지수평활화 모델 계수 선정)

  • Song, Kyung-Bin;Kwon, Oh-Sung;Park, Jeong-Do
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.62 no.2
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    • pp.149-154
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    • 2013
  • Short term load forecasting for electric power demand is essential for stable power system operation and efficient power market operation. High accuracy of the short term load forecasting can keep the power system more stable and save the power market operation cost. We propose an optimal coefficient selection method for exponential smoothing model in short term load forecasting on weekdays. In order to find the optimal coefficient of exponential smoothing model, load forecasting errors are minimized for actual electric load demand data of last three years. The proposed method are verified by case studies for last three years from 2009 to 2011. The results of case studies show that the average percentage errors of the proposed load forecasting method are improved comparing with errors of the previous methods.

Development of Short-Term Load Forecasting Algorithm Using Hourly Temperature (시간대별 기온을 이용한 전력수요예측 알고리즘 개발)

  • Song, Kyung-Bin
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.63 no.4
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    • pp.451-454
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    • 2014
  • Short-term load forecasting(STLF) for electric power demand is essential for stable power system operation and efficient power market operation. We improved STLF method by using hourly temperature as an input data. In order to using hourly temperature to STLF algorithm, we calculated temperature-electric power demand sensitivity through past actual data and combined this sensitivity to exponential smoothing method which is one of the STLF method. The proposed method is verified by case study for a week. The result of case study shows that the average percentage errors of the proposed load forecasting method are improved comparing with errors of the previous methods.