산업공학 (IE interfaces)

  • 제24권4호
  • /
  • Pages.351-361
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  • 2011
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  • 1225-0996(pISSN)
  • /
  • 2234-6465(eISSN)
대한산업공학회 (Korean Institute of Industrial Engineers)
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Support Vector Regression에 기반한 전력 수요 예측

Electricity Demand Forecasting based on Support Vector Regression

  • 이형로 (아주대학교 산업공학과) ;
  • 신현정 (아주대학교 산업공학과)
  • 투고 : 2011.08.31
  • 심사 : 2011.11.02
  • 발행 : 2011.12.01
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초록

Forecasting of electricity demand have difficulty in adapting to abrupt weather changes along with a radical shift in major regional and global climates. This has lead to increasing attention to research on the immediate and accurate forecasting model. Technically, this implies that a model requires only a few input variables all of which are easily obtainable, and its predictive performance is comparable with other competing models. To meet the ends, this paper presents an energy demand forecasting model that uses the variable selection or extraction methods of data mining to select only relevant input variables, and employs support vector regression method for accurate prediction. Also, it proposes a novel performance measure for time-series prediction, shift index, followed by description on preprocessing procedure. A comparative evaluation of the proposed method with other representative data mining models such as an auto-regression model, an artificial neural network model, an ordinary support vector regression model was carried out for obtaining the forecast of monthly electricity demand from 2000 to 2008 based on data provided by Korea Energy Economics Institute. Among the models tested, the proposed method was shown promising results than others.

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피인용 문헌

  1. Electricity Price Prediction Based on Semi-Supervised Learning and Neural Network Algorithms vol.39, pp.1, 2013, https://doi.org/10.7232/JKIIE.2013.39.1.030
  2. Development of Daily Peak Power Demand Forecasting Algorithm using ELM vol.62, pp.4, 2013, https://doi.org/10.5370/KIEEP.2013.62.4.169
  3. Development of Daily Peak Power Demand Forecasting Algorithm with Hybrid Type composed of AR and Neuro-Fuzzy Model vol.63, pp.3, 2014, https://doi.org/10.5370/KIEEP.2014.63.3.189
  4. Development of Daily Peak Power Demand Forecasting Algorithm Considering of Characteristics of Day of Week vol.63, pp.4, 2014, https://doi.org/10.5370/KIEEP.2014.63.4.307
  5. Development of Peak Power Demand Forecasting Model for Special-Day using ELM vol.64, pp.2, 2015, https://doi.org/10.5370/KIEEP.2015.64.2.074
  6. The Prediction and Analysis of the Power Energy Time Series by Using the Elman Recurrent Neural Network vol.41, pp.1, 2018, https://doi.org/10.11627/jkise.2018.41.1.084