Abstract：The construction of campus power consumption supervision platform in China provides data support for the analysis and prediction of campus power consumption. With the help of Microsoft Azure Machine Learning platform, this paper analyzes the power consumption data of Fuzhou University, and then derives two impact factors of power consumption: air temperature and daily schedules. According to the analysis results, this paper defines the air temperature that leads to the sharp growth of power consumption, and then proposes a segmented prediction algorithm based on this idea. The experimental results prove that the segmented prediction algorithm can effectively improve the prediction accuracy of power consumption.
熊甜, 郑松, 徐哲壮, 谢仁栩, 葛永乐. 基于Azure机器学习平台的大学校园用电分析与预测[J]. 电气技术, 2018, 19(5): 5-9.
Xiong Tian, Zheng Song, Xu Zhezhuang, Xie Renxu, Ge Yongle. The analysis and prediction of power consumption in university campus based on azure machine learning platform. Electrical Engineering, 2018, 19(5): 5-9.
2018, Vol. 19 
