The Short-term Load Forecasting of Electric System
- https://doi.org/10.2991/icmmita-16.2016.80How to use a DOI?
- regression analysis; grey prediction model
This thesis firstly analyzes some indexes as daily maximum load, daily minimum load, daily difference between peak and valley and daily load rate in two different regions. And then it makes a stepwise regression analysis on the relationship between the above indexes and various climate factors, so as to obtain equations of linear regression and regression errors in the two regions. After that, the thesis selects some key impacting indexes to analyze the influence of climate on the electric load forecasting. Finally, it makes short-term forecasting on the load of the two regions by the grey predication way, the data of which are compared with the real ones to judge the feasibility of the forecasting methods.
- © 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/).
Cite this article
TY - CONF AU - Zhaoyuan Wang PY - 2017/01 DA - 2017/01 TI - The Short-term Load Forecasting of Electric System BT - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications PB - Atlantis Press SP - 438 EP - 441 SN - 2352-538X UR - https://doi.org/10.2991/icmmita-16.2016.80 DO - https://doi.org/10.2991/icmmita-16.2016.80 ID - Wang2017/01 ER -