Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications

Forecast of Power System Load in Short Term

Authors
Hengshu Ye
Corresponding Author
Hengshu Ye
Available Online January 2017.
DOI
https://doi.org/10.2991/icmmita-16.2016.77How to use a DOI?
Keywords
Sustainable Curve of Electric Power Charge; ARIMA; Regression Analysis
Abstract

.In this paper, according to large quantity of historical statistics, we have established a model which could successfully forecast the power charge in two regions. Because of the different efforts between weekdays, weekends and holidays, we made a piecewise function[1] to decrease the error. The method of 2 times curve fitting was used to analyze the electric power charge of maximum, minimum and average per day by Matlab. Then an ARIMA (Auto Regressive Integrated Moving Average Model) connected with statistics between 2009 to 2014 was set up and verified available by residual analysis. We also take climate factors into consideration. Being supported by huge data base, the model can predict variation tendency of electric power charge efficiently.

Copyright
© 2017, 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 2016 4th International Conference on Machinery, Materials and Information Technology Applications
Series
Advances in Computer Science Research
Publication Date
January 2017
ISBN
978-94-6252-285-5
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmmita-16.2016.77How to use a DOI?
Copyright
© 2017, 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  - Hengshu Ye
PY  - 2017/01
DA  - 2017/01
TI  - Forecast of Power System Load in Short Term
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 424
EP  - 427
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-16.2016.77
DO  - https://doi.org/10.2991/icmmita-16.2016.77
ID  - Ye2017/01
ER  -