Online Boosting Regression Method and Its Application to Load Forecasting in Energy-Intensive Enterprise

FAN Boliang; GAO Feng; KOU Peng

doi:10.13976/j.cnki.xk.2014.0750

Volume 43 Issue 6

Dec. 2014

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INFORMATION AND CONTROL > 2014 > 43(6): 750-756. > DOI: 10.13976/j.cnki.xk.2014.0750 CSTR: 32166.14.xk.2014.0750

FAN Boliang, GAO Feng, KOU Peng. Online Boosting Regression Method and Its Application to Load Forecasting in Energy-Intensive Enterprise[J]. INFORMATION AND CONTROL, 2014, 43(6): 750-756. DOI: 10.13976/j.cnki.xk.2014.0750

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Online Boosting Regression Method and Its Application to Load Forecasting in Energy-Intensive Enterprise

1.
SKLMS, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, China
2.
State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China

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Received Date: October 20, 2013
Revised Date: September 11, 2014
Published Date: December 19, 2014

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Abstract

Abstract

Focusing on the electricity load forecasting in energy-intensive enterprises, we propose an online forecasting algorithm based on the boosting method. The algorithm converts a regression problem such as load forecasting to a binary classification problem via geometric operation, and then it obtains a timely adaptive ensemble max-margin separating plane using an online smooth boosting algorithm. We prove that such a plane is theoretically identical to a regression function for the original load forecasting problem, and also prove that the proposed algorithm has convergence for the training set. Simulation results show that the proposed algorithm improves real-time tracking capability by implementing online updating, and it improves the accuracy and stability of load forecasting through the boosting algorithm. The forecasting error is less than 3%.
- online learning,
- Boosting algorithm,
- regression,
- load forecasting,
- energy-intensive enterprise

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Online Boosting Regression Method and Its Application to Load Forecasting in Energy-Intensive Enterprise

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