Proceedings of the 2017 2nd International Conference on Electrical, Control and Automation Engineering (ECAE 2017)

Power System Short-Term Load Forecasting Based on Multiple Proportions Smoothing Method

Authors
Ye Tang
Corresponding Author
Ye Tang
Available Online December 2017.
DOI
https://doi.org/10.2991/ecae-17.2018.16How to use a DOI?
Keywords
daily load curve; short-term load forecasting; multiple proportions smoothing method
Abstract

Aiming at the problem of short-term load forecasting in power system, this paper propose a load prediction method based on multiple proportions smoothing method. We perform multiple linear regression analysis and partial correlation analysis on the load with respect to the weather, and found the meteorological factors that are highly correlated with the power load. Then, we establish a primary power system short-term load forecasting model by using the ratio multiplication smoothing method.

Copyright
© 2018, 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/).

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Volume Title
Proceedings of the 2017 2nd International Conference on Electrical, Control and Automation Engineering (ECAE 2017)
Series
Advances in Engineering Research
Publication Date
December 2017
ISBN
978-94-6252-458-3
ISSN
2352-5401
DOI
https://doi.org/10.2991/ecae-17.2018.16How to use a DOI?
Copyright
© 2018, 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  - Ye Tang
PY  - 2017/12
DA  - 2017/12
TI  - Power System Short-Term Load Forecasting Based on Multiple Proportions Smoothing Method
BT  - Proceedings of the 2017 2nd International Conference on Electrical, Control and Automation Engineering (ECAE 2017)
PB  - Atlantis Press
SP  - 77
EP  - 78
SN  - 2352-5401
UR  - https://doi.org/10.2991/ecae-17.2018.16
DO  - https://doi.org/10.2991/ecae-17.2018.16
ID  - Tang2017/12
ER  -