@inproceedings{1f5779aaef6b4b35a3d82cb71300f04b,
title = "Smart grid data analysis and prediction modeling",
abstract = "With the rapiddevelopment of power grid, prediction ofelectric quantity changes has become increasingly important. High-performance power grid systems can improve economic effectiveness and operational efficiency through accurate prediction. This paper proposes a prediction model based on temperature, humidity, time, and the number of people. On account of the standards of support vector machine (SVM) and the HBase platform, we have implemented a forecasting model and designed simulative experiments. The experimental results show that time and variation in the number of people has a remarkable influence on prediction, while temperature and humidityhardly have any effects.",
keywords = "Data mining, HBase data storage, Machine learning, Power prediction, SVM",
author = "Hang Yang and Ping Li and Anum Masood and Yuning Xiao and Bin Sheng and Qichen Yu",
year = "2016",
month = dec,
doi = "10.1109/PIC.2016.7949559",
language = "English",
series = "PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "541--544",
editor = "Yinglin Wang and Yaoru Sun",
booktitle = "PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing",
note = "4th IEEE International Conference on Progress in Informatics and Computing, PIC 2016 ; Conference date: 23-12-2016 Through 25-12-2016",
}