Application of Support Vector Machine Based on Particle Swarm Optimization in Low Voltage Line Loss Prediction
- 10.2991/jimet-15.2015.35How to use a DOI?
- support vector machine; particle swarm optimization; line loss prediction
As low voltage line loss calculation is the difficulty of line loss, accurate prediction of low voltage line loss rate can guide energy-saving and consumption-reducing work effectively. In this paper, the support vector machine (SVM) Parameters Optimization Algorithm based on particle swarm optimization (PSO) is used to predict the low voltage line loss rate. After the analysis of the related factors that affecting line loss, the power supply average length of lines average capacity of transformers and maximum load are selected as parameters for the training of SVM prediction model, then use the line loss prediction model to predict the low voltage line loss rate. Predict results of model testing which uses part of the known data in typical year show the average prediction error is only1.92%, which can give a strong support for this line loss prediction model.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Tan Min AU - Wang Xinghua AU - Li Qing AU - Guo Lexin AU - Yu Tao AU - Feng Yongkun PY - 2015/12 DA - 2015/12 TI - Application of Support Vector Machine Based on Particle Swarm Optimization in Low Voltage Line Loss Prediction BT - Proceedings of the 2015 Joint International Mechanical, Electronic and Information Technology Conference PB - Atlantis Press SP - 193 EP - 196 SN - 2352-538X UR - https://doi.org/10.2991/jimet-15.2015.35 DO - 10.2991/jimet-15.2015.35 ID - Min2015/12 ER -