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

The Short-term Load Forecasting of Electric System

Authors
Zhaoyuan Wang
Corresponding Author
Zhaoyuan Wang
Available Online January 2017.
DOI
https://doi.org/10.2991/icmmita-16.2016.80How to use a DOI?
Keywords
regression analysis; grey prediction model
Abstract

This thesis firstly analyzes some indexes as daily maximum load, daily minimum load, daily difference between peak and valley and daily load rate in two different regions. And then it makes a stepwise regression analysis on the relationship between the above indexes and various climate factors, so as to obtain equations of linear regression and regression errors in the two regions. After that, the thesis selects some key impacting indexes to analyze the influence of climate on the electric load forecasting. Finally, it makes short-term forecasting on the load of the two regions by the grey predication way, the data of which are compared with the real ones to judge the feasibility of the forecasting methods.

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-16.2016.80How 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  - Zhaoyuan Wang
PY  - 2017/01
DA  - 2017/01
TI  - The Short-term Load Forecasting of Electric System
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 438
EP  - 441
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-16.2016.80
DO  - https://doi.org/10.2991/icmmita-16.2016.80
ID  - Wang2017/01
ER  -