Forecasting of photovoltaic power yield using dynamic neural networks

Al-Messabi, N., Li, Y. , El-Amin, I. and Goh, C.S.F. (2012) Forecasting of photovoltaic power yield using dynamic neural networks. In: The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-5. ISBN 9781467314886 (doi: 10.1109/IJCNN.2012.6252406)

Abstract

The importance of predicting the output power of Photovoltaic (PV) plants is crucial in modern power system applications. Predicting the power yield of a PV generation system helps the process of dispatching the power into a grid with improved efficiency in generation planning and operation. This work proposes the use of intelligent tools to forecast the real power output of PV units. These tools primarily comprise dynamic neural networks which are capable of time-series predictions with good reliability. This paper begins with a brief review of various methods of forecasting solar power reported in literature. Results of preliminary work on a 5kW PV panel at King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia, is presented. Focused Time Delay and Distributed Time Delay Neural Networks were used as a forecasting tool for this study and their performance was compared with each other.

• Item Type: Book Sections
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Goh, Dr Cindy Sf and Li, Professor Yun
• Authors: Al-Messabi, N., Li, Y., El-Amin, I., and Goh, C.S.F.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Neural Networks (IJCNN), The 2012 International Joint Conference on
• Publisher: IEEE
• ISBN: 9781467314886