Temperature prediction of PV/T component based on RBF neural network
Jiyong Li, Zhendong Zhao, Junpei Nan, Yisheng Li, Yunfeng Tang
Available Online April 2017.
- https://doi.org/10.2991/iceesd-17.2017.89How to use a DOI?
- PV/T; Optimal control; Temperature prediction; RBF neural network
- In the comprehensive utilization of solar photovoltaic-thermal hybrid (PV/T) system research, PV/T component temperature control is very important to improve the efficiency and thermal efficiency. For PV/T system is a big inertia system, temperature control effect needs a period of time. So accurately predicting PV/T component temperature in advance is advantageous to the PV/T system optimization control. In this paper, based on RBF neural network, the sunny weather temperature prediction model is established, which can forecast the component temperature after 15 minutes. By comparing measured data with the predicted data, the model has a high accuracy in prediction, which can provide technical support for the follow-up PV/T system research.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Jiyong Li AU - Zhendong Zhao AU - Junpei Nan AU - Yisheng Li AU - Yunfeng Tang PY - 2017/04 DA - 2017/04 TI - Temperature prediction of PV/T component based on RBF neural network BT - Proceedings of the 2017 6th International Conference on Energy, Environment and Sustainable Development (ICEESD 2017) PB - Atlantis Press SP - 469 EP - 473 SN - 2352-5401 UR - https://doi.org/10.2991/iceesd-17.2017.89 DO - https://doi.org/10.2991/iceesd-17.2017.89 ID - Li2017/04 ER -