Forecast of Power System Load in Short Term

DOI

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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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  - 10.2991/icmmita-16.2016.77
ID  - Ye2017/01
ER  -