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Contents:2019,Vol:24,Issue(2):101-108 |
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Citation: |
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HOU Luting, GAO Junwei. Forecasting of Short-term Load based on LMD and BBO-RBF Model[J]. International Journal of Plant Engineering and Management, 2019, 24(2): 101-108 |
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| Forecasting of Short-term Load based on LMD and BBO-RBF Model |
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| HOU Luting1, GAO Junwei2 |
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1. College of Automation and Electrical Engineering, Qingdao University, Shandong Qingdao 266071, China;
2. Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China |
| Abstract: |
| Short-term load forecasting is a basis of power system dispatching and operation. In order to improve the short term power load precision, a novel approach for short-term load forecasting is presented based on local mean decomposition (LMD) and the radial basis function neural network method (RBFNN). Firstly, the decomposition of LMD method based on characteristics of load data then the decomposed data are respectively predicted by using the RBF network model and predicted by using the BBO-RBF network model. The simulation results show that the RBF network model optimized by using BBO algorithm is optimized in error performance index, and the prediction accuracy is higher and more effective. |
| Key words:
short-term load
local mean decomposition
radial basis function neural network
BBO algorithm
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| Received: 2019-03-20
Revised:
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| DOI: 10.13434/j.cnki.1007-4546.2019.0205 |
| Corresponding author:
Email： |
Author description: HOU Luting is a master candidate in College of Automation and Electrical Engineering, Qingdao University. His research direction is pattern recognition and intelligent information processing. 601303533@qq.com
GAO Junwei, Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University. His research direction is pattern recognition and intelligent information processing. qdgao163@163.com
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