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

被引：0

作者：

Dragomir, Otilia Elena ^{[1
]}

Dragomir, Florin ^{[1
]}

Minca, Eugenia ^{[1
]}

机构：

[1] Valahia Univ Targoviste, Fac Elect Engn, Automat Comp Sci & Elect Engn Dept, 18 Unirii Ave, Targoviste, Romania

来源：

ICINCO 2011: PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, VOL 2 | 2011年

关键词：

RBF; Neural networks; Load renewable energy; Forecasting;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：409 / 412

页数：4

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