• Title/Summary/Keyword: Load forecasting

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Weekly Maximum Electric Load Forecasting for 104 Weeks by Seasonal ARIMA Model (계절 ARIMA 모형을 이용한 104주 주간 최대 전력수요예측)

  • Kim, Si-Yeon;Jung, Hyun-Woo;Park, Jeong-Do;Baek, Seung-Mook;Kim, Woo-Seon;Chon, Kyung-Hee;Song, Kyung-Bin
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.28 no.1
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    • pp.50-56
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    • 2014
  • Accurate midterm load forecasting is essential to preventive maintenance programs and reliable demand supply programs. This paper describes a midterm load forecasting method using autoregressive integrated moving average (ARIMA) model which has been widely used in time series forecasting due to its accuracy and predictability. The various ARIMA models are examined in order to find the optimal model having minimum error of the midterm load forecasting. The proposed method is applied to forecast 104-week load pattern using the historical data in Korea. The effectiveness of the proposed method is evaluated by forecasting 104-week load from 2011 to 2012 by using historical data from 2002 to 2010.

A Study on Short-Term Load Forecasting System Using Data Mining (데이터 마이닝을 이용한 단기부하예측 시스템 연구)

  • Kim, Do-Wan;Park, Jin-Bae;Kim, Juhg-Chan;Joo, Young-Hoon
    • Proceedings of the KIEE Conference
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    • 2003.11c
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    • pp.588-591
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    • 2003
  • This paper presents a new short-term load forecasting system using data mining. Since the electric load has very different pattern according to the day, it definitely gives rise to the forecasting error if only one forecasting model is used. Thus, to resolve this problem, the fuzzy model-based classifier and predictor are proposed for the forecasting of the hourly electric load. The proposed classifier is the multi-input and multi-output fuzzy system of which the consequent part is composed of the Bayesian classifier. The proposed classifier attempts to categorize the input electric load into Monday, Tuesday$\sim$Friday, Saturday, and Sunday electric load, Then, we construct the Takagi-Sugeno (T-S) fuzzy model-based predictor for each class. The parameter identification problem is converted into the generalized eigenvalue problem (GEVP) by formulating the linear matrix inequalities (LMIs). Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.

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Short-Term Load Forecasting Using Neural Networks and the Sensitivity of Temperatures in the Summer Season (신경회로망과 하절기 온도 민감도를 이용한 단기 전력 수요 예측)

  • Ha Seong-Kwan;Kim Hongrae;Song Kyung-Bin
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.54 no.6
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    • pp.259-266
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    • 2005
  • Short-term load forecasting algorithm using neural networks and the sensitivity of temperatures in the summer season is proposed. In recent 10 years, many researchers have focused on artificial neural network approach for the load forecasting. In order to improve the accuracy of the load forecasting, input parameters of neural networks are investigated for three training cases of previous 7-days, 14-days, and 30-days. As the result of the investigation, the training case of previous 7-days is selected in the proposed algorithm. Test results show that the proposed algorithm improves the accuracy of the load forecasting.

Short-term 24 hourly Load forecasting for holidays using fuzzy linear regression (퍼지 선형회귀분석법을 이용한 특수일의 24시간 단기수요예측)

  • Ha, Seong-Kwan;Song, Kyung-Bin;Kim, Byung-Su
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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    • 2004.05a
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    • pp.434-436
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    • 2004
  • Load forecasting is essential in the electricity market for the participants to manage the market efficiently and stably. The percentage errors of 24 hourly load forecasting for holidays is relatively large. In this paper, we propose the maximum and minimum load forecasting method for holidays using a fuzz linear regression algorithm. 24 hourly loads are forecasted from the maximum and minimum loads and the 24 hourly normalized values. The proposed algorithm is tested for 24 hourly load forecasting in 1996. The test results show the proposed algorithm improves the accuracy of the load forecasting.

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The Study on Load Forecasting Using Artificial Intelligent Algorithm (지능형 알고리즘을 이용한 전력 소비량 예측에 관한 연구)

  • Lee, Jae-Hyun
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2009.10a
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    • pp.720-722
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    • 2009
  • Optimal operation of electric power generating plants is very essential for any power utility organization to reduce input costs and possibly the prices of electricity in general. This paper developed models for load forecasting using neural networks approach. This model is tested using actual load data of the Busan and weather data to predict the load of the Busan for one month in advance. The test results showed that the neural network forecasting approach is more suitable and efficient for a forecasting application.

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A Stochastic Pplanning Method for Semand-side Management Program based on Load Forecasting with the Volatility of Temperature (온도변동성을 고려한 전력수요예측 기반의 확률론적 수요관리량 추정 방법)

  • Wi, Young-Min
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.64 no.6
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    • pp.852-856
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    • 2015
  • Demand side management (DSM) program has been frequently used for reducing the system peak load because it gives utilities and independent system operator (ISO) a convenient way to control and change amount of electric usage of end-use customer. Planning and operating methods are needed to efficiently manage a DSM program. This paper presents a planning method for DSM program. A planning method for DSM program should include an electric load forecasting, because this is the most important factor in determining how much to reduce electric load. In this paper, load forecasting with the temperature stochastic modeling and the sensitivity to temperature of the electric load is used for improving load forecasting accuracy. The proposed planning method can also estimate the required day, hour and total capacity of DSM program using Monte-Carlo simulation. The results of case studies are presented to show the effectiveness of the proposed planning method.

Short-Term Load Forecasting for the Consecutive Holidays Considering Businesses' Operation Rates of Industries (산업체의 조업률을 반영한 연휴의 단기 전력수요예측)

  • Song, Kyung-Bin;Lim, Jong-Hun
    • 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.

A Study on Long-Term Spatial Load Forecasting Using Trending Method (추세분석법에 의한 영역의 장기 수요예측)

  • Hwang Kab-Ju;Choi Soo-Keon
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.53 no.11
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    • pp.604-609
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    • 2004
  • This paper suggests a long-term distribution area load forecasting algorithm which offers basic data for distribution planning of power system. To build forecasting model, 4-level hierarchical spatial structure is introduced: System, Region, Area, and Substation. And, each spatial load can be decided proportional to its portion in the higher level. This paper introduces the horizon year loads to improve the forecasting results. And, this paper also introduces an effective load transfer algorithm to improve forecasting stability in case of new or stopped substations. The proposed model is applied to the load forecasting of KEPCO system composed of 16 regions, 85 areas and 761 substations, and the results are compared with those of econometrics model to verify its validity.

Short-term Peak Power Demand Forecasting using Model in Consideration of Weather Variable (기상 변수를 고려한 모델에 의한 단기 최대전력수요예측)

  • 고희석;이충식;최종규;지봉호
    • Journal of the Institute of Convergence Signal Processing
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    • v.2 no.3
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    • pp.73-78
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    • 2001
  • BP neural network model and multiple-regression model were composed for forecasting the special-days load. Special-days load was forecasted using that neural network model made use of pattern conversion ratio and multiple-regression made use of weekday-change ratio. This methods identified the suitable as that special-days load of short and long term was forecasted with the weekly average percentage error of 1∼2[%] in the weekly peak load forecasting model using pattern conversion ratio. But this methods were hard with special-days load forecasting of summertime. therefore it was forecasted with the multiple-regression models. This models were used to the weekday-change ratio, and the temperature-humidity and discomfort-index as explanatory variable. This methods identified the suitable as that compared forecasting result of weekday load with forecasting result of special-days load because months average percentage error was alike. And, the fit of the presented forecast models using statistical tests had been proved. Big difficult problem of peak load forecasting had been solved that because identified the fit of the methods of special-days load forecasting in the paper presented.

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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.