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

DOI

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.

Copyright

© 2017, 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  - 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  - 10.2991/iceesd-17.2017.89
ID  - Li2017/04
ER  -