FORCASTING OF RENEWABLE ENERGY LOAD WITH RADIAL BASIS FUNCTION (RBF) NEURAL NETWORKS

Otilia Elena Dragomir, Florin Dragomir, Eugenia Minca

Abstract

This paper focus on radial- basis function (RBF) neural networks, the most popular and widely-used paradigms in many applications, including renewable energy forecasting. It provides an analysis of short term load forecasting STLF performances of RBF neural networks. Precisely, the goal is to forecast the DPcg (difference between the electricity produced from renewable energy sources and consumed), for short- term horizon. The forecasting accuracy and precision, in capturing nonlinear interdependencies between the load and solar radiation of these neural networks are illustrated and discussed using a data based obtain from an experimental photovoltaic amphitheatre of minimum dimension 0.4kV/10kW.

References

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Paper Citation


in Harvard Style

Elena Dragomir O., Dragomir F. and Minca E. (2011). FORCASTING OF RENEWABLE ENERGY LOAD WITH RADIAL BASIS FUNCTION (RBF) NEURAL NETWORKS . In Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-8425-75-1, pages 409-412. DOI: 10.5220/0003534204090412


in Bibtex Style

@conference{icinco11,
author={Otilia Elena Dragomir and Florin Dragomir and Eugenia Minca},
title={FORCASTING OF RENEWABLE ENERGY LOAD WITH RADIAL BASIS FUNCTION (RBF) NEURAL NETWORKS},
booktitle={Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2011},
pages={409-412},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003534204090412},
isbn={978-989-8425-75-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - FORCASTING OF RENEWABLE ENERGY LOAD WITH RADIAL BASIS FUNCTION (RBF) NEURAL NETWORKS
SN - 978-989-8425-75-1
AU - Elena Dragomir O.
AU - Dragomir F.
AU - Minca E.
PY - 2011
SP - 409
EP - 412
DO - 10.5220/0003534204090412