The Real-time Forecast of Photovoltaic Output Based on Empirical Mode Decomposition

DOI

10.2991/iwmecs-15.2015.133How to use a DOI?

Keywords

Empirical Mode Decomposition, Support Vector Machine, real-time forecast.

Abstract

The randomness and volatility of photovoltaic (PV) output brings difficulties to do real-time forecast. This paper proposed a new method called EMD-SVM-GA to forecast PV output real-time. Three steps were needed to carry out the method. Firstly, decompose historical PV output data sequence into several Intrinsic Mode Functions (IMF) and a Residual Component based on Empirical Mode Decomposition (EMD) theory. Secondly, construct Support Vector Machine (SVM) models for every component and search best parameters by Genetic Algorithm. Then superpose all forecast results to obtain the final data. Examples show that EMD-SVM-GA method has better performance and higher accuracy.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Cui Du
AU  - Zongqi Liu
PY  - 2015/10
DA  - 2015/10
TI  - The Real-time Forecast of Photovoltaic Output Based on Empirical Mode Decomposition
BT  - Proceedings of the 2015 2nd International Workshop on Materials Engineering and Computer Sciences
PB  - Atlantis Press
SP  - 666
EP  - 669
SN  - 2352-538X
UR  - https://doi.org/10.2991/iwmecs-15.2015.133
DO  - 10.2991/iwmecs-15.2015.133
ID  - Du2015/10
ER  -