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A power quality forecasting model as an integrate part of active demand side management using Artificial Intelligence Technique - Multilayer Neural Network with Backpropagation Learning Algorithm

Authors: Jindrich Stuchly; Stanislav Misak; Tomas Vantuch; Tomas Burianek;

A power quality forecasting model as an integrate part of active demand side management using Artificial Intelligence Technique - Multilayer Neural Network with Backpropagation Learning Algorithm

Abstract

This paper presents a power quality forecasting model with using Artificial Intelligence Technique, more precisely the Multilayer Neural Network with Backpropagation Learning Algorithm. This forecasting model is used as a supporting tool for a keeping of power quality parameters within the limits in the Off-Grid systems with renewables sources connected via AC By-Pass topology. Results of the most important power quality parameters forecasting are introduced in this paper. The developed algorithm of this model will be implemented into system for controlling the power flows inside the Off-Grid systems operated under Active Demand Side Management.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
10
Top 10%
Top 10%
Top 10%
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