Short-term Load Forecasting of Electric Power System Based On Meteorological Factors

DOI

10.2991/icmmbe-16.2016.38How to use a DOI?

Keywords

Short-term load forecasting; Meteorological factor; Wavelet analysis; Gray relational degree; Dynamic neural network

Abstract

In this paper, it analyzes the characteristics of the changes of meteorological factors and short-term load. Besides, it studies the relationship between meteorological factors (temperature, humidity, rainfall) and short-term load forecasting, so as to select the important meteorological factors, after which, it forecasts the short-term load according to the similar meteorological factors. Next, by means of wavelet transform and Fourier analysis, the variation characteristics of load and meteorological factors are obtained. And then, the influence factors are defined to measure the effect of various meteorological factors on load by using regression coefficient. Finally, through the gray relational degree, the correlation degree is selected in the date of 0.98 or more. Then the load data of these dates are used to train the neural network repeatedly and repeatedly to get more accurate predictive value.

Copyright

© 2016, 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  - Jing Liu
PY  - 2016/09
DA  - 2016/09
TI  - Short-term Load Forecasting of Electric Power System Based On Meteorological Factors
BT  - Proceedings of the6th International Conference on Mechatronics, Materials, Biotechnology and Environment (ICMMBE 2016)
PB  - Atlantis Press
SP  - 195
EP  - 200
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmbe-16.2016.38
DO  - 10.2991/icmmbe-16.2016.38
ID  - Liu2016/09
ER  -