Short - term load forecasting of power system

DOI

10.2991/ammee-17.2017.83How to use a DOI?

Keywords

the partial least squares regression analysis, ARMA time series, BP neural network.

Abstract

Short-term load forecasting is of great significance to the operation and analysis of power system to improve the accuracy of load forecasting. An Important Means to Guarantee the Scientific Decision of Power System Optimization. This paper aims to analyze the load fluctuation of the two regions and the relationship between meteorological factors, holiday factors and periodicity, and set up the least squares regression analysis model, time series model and BP neural network model to short - term load forecasting of power system.

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  - Zongheng Jiang
PY  - 2017/06
DA  - 2017/06
TI  - Short - term load forecasting of power system
BT  - Proceedings of the Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)
PB  - Atlantis Press
SP  - 445
EP  - 447
SN  - 2352-5401
UR  - https://doi.org/10.2991/ammee-17.2017.83
DO  - 10.2991/ammee-17.2017.83
ID  - Jiang2017/06
ER  -