Research on Power Short-term Prediction of the Photovoltaic System Based on Grey Relational Analysis and Quantum Particle Swarm Optimization

DOI

10.2991/seee-15.2015.23How to use a DOI?

Keywords

photovoltaic power prediction; grey relational analysis; battery characteristics; quantum particle swarm optimization; Support vector machine; Wulan photovoltaic power station

Abstract

Output power of Photovoltaic generation system is influenced by temperature, humidity, solar radiation intensity and so on. The effects of three kinds of external climate conditions, including temperature, humidity, solar radiation intensity, on photovoltaic output power were anal sized in detail in this paper, and then similar days for photovoltaic power prediction were selected based on grey relational analysis. The quantum particle swarm optimization method for optimizing kernel parameters of support vector machine was immediately introduced. In line with the data of similar days and optimization parameters of kernel function, a new power short-term prediction method of the photovoltaic system based on grey relational analysis and quantum particle swarm optimization was put up in this paper. According to the data of photovoltaic output power and meteorological monitoring data of Wulan photovoltaic power station, the method mentioned is likely verified. Instances proved that this new power short-term prediction method has great advantages in terms of speed, accuracy and stability.

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/).

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TY  - CONF
AU  - Qingwu Gong
AU  - Jiazhi Lei
AU  - Haining Zhang
AU  - Yang Lei
AU  - Si Tan
PY  - 2015/10
DA  - 2015/10
TI  - Research on Power Short-term Prediction of the Photovoltaic System Based on Grey Relational Analysis and Quantum Particle Swarm Optimization
BT  - Proceedings of the 2015 International Conference on Sustainable Energy and Environmental Engineering
PB  - Atlantis Press
SP  - 91
EP  - 95
SN  - 2352-5401
UR  - https://doi.org/10.2991/seee-15.2015.23
DO  - 10.2991/seee-15.2015.23
ID  - Gong2015/10
ER  -