Short-term Load Forecasting of Electric Power System Based On Meteorological Factors
- 10.2991/icmmbe-16.2016.38How to use a DOI?
- Short-term load forecasting; Meteorological factor; Wavelet analysis; Gray relational degree; Dynamic neural network
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.
- © 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/).
Cite this article
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 -