With the rapid development of power grid, prediction of electric 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 humidity hardly have any effects. Copyright © 2016 IEEE.
|Title of host publication||Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing|
|Place of Publication||Beijing|
|Publisher||Institute of Electrical and Electronics Engineers, Inc|
|ISBN (Print)||9781509034840, 9781509034833|
|Publication status||Published - 2016|
Support vector machines
CitationYang, H., Li, P., Masood, A., Xiao, Y., Sheng, B., & Yu, Q. (2016). Smart grid data analysis and prediction modeling. In Y. Wang, & Y. Sun (Eds.), Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing (pp. 541-544). Beijing: Institute of Electrical and Electronics Engineers, Inc.
- Power prediction
- Data mining
- HBase data storage
- Machine learning