Proceedings of the 2016 International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2016)

Short-Term Bus Load Matching and Forecasting Model

Authors
Ran Li, Chenjun Sun, Yang Liu, Lilin Peng, Ming Zeng
Corresponding Author
Ran Li
Available Online September 2016.
DOI
https://doi.org/10.2991/iccia-16.2016.23How to use a DOI?
Keywords
Short-term bus load forecasting; Least squares support vector machine (LSSVM); Matching model; Subtractive clustering.
Abstract
Bus load forecasting is the foundation to make power grid to be secure, economic and efficient, and also is the concrete measure and effective way to promote national energy saving and emissions reduction. Based on daily load curve of typical areas, and by means of subtractive clustering, the regional bus load was classified and bus load matching model was constructed. Furthermore, bus load forecasting model with the least squares support vector machine model (LSSVM) was put forward. Finally, the validity of load matching and forecasting model was verified by numerical examples.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2016)
Part of series
Advances in Computer Science Research
Publication Date
September 2016
ISBN
978-94-6252-240-4
ISSN
2352-538X
DOI
https://doi.org/10.2991/iccia-16.2016.23How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Ran Li
AU  - Chenjun Sun
AU  - Yang Liu
AU  - Lilin Peng
AU  - Ming Zeng
PY  - 2016/09
DA  - 2016/09
TI  - Short-Term Bus Load Matching and Forecasting Model
BT  - 2016 International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2016)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccia-16.2016.23
DO  - https://doi.org/10.2991/iccia-16.2016.23
ID  - Li2016/09
ER  -