论文信息 - Forcasting of Renewable Energy Load with Radial Basis Function (RBF) Neural Networks

Forcasting of Renewable Energy Load with Radial Basis Function (RBF) Neural Networks

This paper focus on radialbasis 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 shortterm 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.

Eugenia Minca | Otilia Elena Dragomir | Florin Dragomir

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