Proceedings of the 2017 6th International Conference on Energy, Environment and Sustainable Development (ICEESD 2017)

Temperature prediction of PV/T component based on RBF neural network

Authors
Jiyong Li, Zhendong Zhao, Junpei Nan, Yisheng Li, Yunfeng Tang
Corresponding Author
Jiyong Li
Available Online April 2017.
DOI
https://doi.org/10.2991/iceesd-17.2017.89How to use a DOI?
Keywords
PV/T; Optimal control; Temperature prediction; RBF neural network
Abstract
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.

Download article (PDF)

Proceedings
2017 6th International Conference on Energy, Environment and Sustainable Development (ICEESD 2017)
Part of series
Advances in Engineering Research
Publication Date
April 2017
ISBN
978-94-6252-328-9
ISSN
2352-5401
DOI
https://doi.org/10.2991/iceesd-17.2017.89How to use a DOI?
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  - 2017 6th International Conference on Energy, Environment and Sustainable Development (ICEESD 2017)
PB  - Atlantis Press
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  -