Improved genetic algorithm-based research on optimization of least square support vector machines: an application of load forecasting

In this paper, the load forecasting model is established to increase the precision of meteorological impacts, temperature, short-term power load forecasting, working and holiday factors by considering the power load. Further, IGA-LS-SVM is proposed which is a short-term power load forecasting techni...

Full description

Saved in:
Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2021-09, Vol.25 (18), p.11997-12005
Main Authors: Bao-De, Lin, Xin-Yang, Zhang, Mei, Zhang, Hui, Li, Guang-Qian, Lu
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computational Intelligence
Control
Engineering
Focus
Mathematical Logic and Foundations
Mechatronics
Robotics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, the load forecasting model is established to increase the precision of meteorological impacts, temperature, short-term power load forecasting, working and holiday factors by considering the power load. Further, IGA-LS-SVM is proposed which is a short-term power load forecasting technique based on AI algorithm. And to increase the forecast accuracy and generalization capability of LS-SVM, we applied the adopted mutation probability and new coding technology to the parameter optimization of LS-SVM. The temperature, load, weather state, working and holiday days be taken as prediction model as input, and load value was predicted output. We selected the sample data from meteorological information and historical load of a city in Yunnan province. By results, the prediction verifies the good prediction effect when associated with existing BP algorithm and the proposed IGA- LS-SVM algorithm yields a value 0.8274 more significant than all others, which is appropriate for short-term power load prediction.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-021-05674-9