A Method to Predict the Intermittent Power by Classification Model

Author

Hang Yang ; Fuzheng Zhang ; Aidong Xu ; Cai Yuan ; Chuanlin Chen

Author_Institution

Electr. Power Res. Inst., China Southern Power Grid, Guangzhou, China

fYear

2014

fDate

8-10 Nov. 2014

Firstpage

28

Lastpage

33

Abstract

More and more power plants have been constructed and generated by intermittent energy. As a clean and renewable energy, such sources as wind and solar are favored in the new generation of power grid system. However, influenced by factors of geography, circumstance and climates, the renewable energy has the characteristics of intermittency, volatility and uncontrollability, which reduce the efficient utilization of intermittent energy. This paper uses data mining methods to predict the level of power generation from solar energy, by analyzing the information collected from distributed power plants. Investigating the real-world power grid dataset, the experimental result verifies the feasibility of the proposed method for improving the utilization of intermittent energy.

Keywords

data analysis; data mining; distributed power generation; pattern classification; power engineering computing; power grids; solar power; classification model; clean energy; climate; data mining method; distributed power plants; geography; information analysis; intermittency; intermittent energy utilization; intermittent power prediction; power generation level prediction; power grid dataset; power grid system; renewable energy sources; solar energy; uncontrollability; volatility; Analytical models; Data models; Load modeling; Power generation; Power grids; Predictive models; classification; data mining; electric power system; intermittent energy;

fLanguage

English

Publisher

ieee

Conference_Titel

P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2014 Ninth International Conference on

Conference_Location

Guangdong

Type

conf

DOI

10.1109/3PGCIC.2014.33

Filename

7024554