MATEC Web Conf.
Volume 139, 20172017 3rd International Conference on Mechanical, Electronic and Information Technology Engineering (ICMITE 2017)
|Number of page(s)||4|
|Published online||05 December 2017|
Analysis and prediction of the summer cooling load characteristics
1 State Grid Sichuan Economic Research Institute SGSERI Chengdu, China
2 College of Management Science, Chengdu University of Technology Chengdu, China
* Corresponding author: firstname.lastname@example.org
Nowadays, cooling load is mainly attributable to upgrade of a new high of power grid load. An accurate calculation and prediction of cooling load can better meet power demands at peak hours in summer. Based on the historical summer cooling load and temperature data of Sichuan province, the characteristics of summer cooling load and temperature in Sichuan are examined firstly. Next, the random and nonlinear relations of power load are studied. Finally, the BP neural network model optimized by the genetic algorithm (GA) is employed to achieve accurate prediction of summer cooling load in Sichuan province. Results suggest that cooling load is closely connected with temperature and air-conditioner ownership, and that the BP neural network optimized by the GA is effective in predicting summer cooling load. To sum up, this research can provide solid basis for power load scheduling.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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