Authors:
Otilia Elena Dragomir
;
Florin Dragomir
and
Eugenia Minca
Affiliation:
Valahia University of Targoviste, Romania
Keyword(s):
RBF, Neural networks, Load renewable energy, Forecasting.
Related
Ontology
Subjects/Areas/Topics:
Energy Efficiency and Green Manufacturing
;
Environmental Monitoring and Control
;
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Neural Networks Based Control Systems
;
Signal Processing, Sensors, Systems Modeling and Control
;
Time Series and System Modeling
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.