Short Term Load Forecasting of Power System

DOI

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.

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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TY  - CONF
AU  - Jiahui Fan
PY  - 2016/07
DA  - 2016/07
TI  - Short Term Load Forecasting of Power System
BT  - Proceedings of the 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  - 10.2991/iccia-17.2017.126
ID  - Fan2016/07
ER  -