Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)

Short Term Load Forecasting of Power System

Authors
Jiahui Fan
Corresponding Author
Jiahui Fan
Available Online July 2016.
DOI
https://doi.org/10.2991/iccia-17.2017.126How to use a DOI?
Keywords
Load forecasting, meteorological factors, BP neural network, similar days.
Abstract
In this paper, short-term load forecasting of power system considering meteorological factors is studied. The power system load is divided into three parts: basic component, weather sensitive component and random component. Then the correction strategy of similar days is introduced and the meteorological factors is considered to improve the original BP model. And the similar days are determined according to the periodic characteristic of the load value and the grey relational analysis. Finally, by comparing the predicted data with the actual data, it is proved that the prediction model agrees with the actual situation and has higher prediction accuracy.
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Proceedings
2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)
Part of series
Advances in Computer Science Research
Publication Date
July 2016
ISBN
978-94-6252-361-6
ISSN
2352-538X
DOI
https://doi.org/10.2991/iccia-17.2017.126How 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  - Jiahui Fan
PY  - 2016/07
DA  - 2016/07
TI  - Short Term Load Forecasting of Power System
BT  - 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)
PB  - Atlantis Press
SP  - 728
EP  - 731
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccia-17.2017.126
DO  - https://doi.org/10.2991/iccia-17.2017.126
ID  - Fan2016/07
ER  -