Abstract
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.
| Original language | English |
|---|---|
| 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 |
| Pages | 541-544 |
| ISBN (Print) | 9781509034840, 9781509034833 |
| Publication status | Published - 2016 |
Citation
Yang, 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.Keywords
- Power prediction
- Data mining
- HBase data storage
- Machine learning
- SVM
