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/).
Cite this article
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 -