Acad. Paul Dan CRISTEA, PhD, Prof. Rodica TUDUCE, PhD, Assoc. Prof. Dumitru Iulian NASTAC, PhD
Rezumat
Lucrarea prezinta un sistem care utilizeaza o retea neurala (RN) pentru invatarea eficienta a unei secvente nestationare. Sistemul are potentialul de a invata, clasifica si prezice orice semnale stationare sau nestationare, in particular curbele de variatie in timp a sarcinii din sistemele electro-energetice, la diferite scale de timp. O mare varietate de aplicatii de clasificare si predictie a secventelor nestationare pot fi rezolvate intr-un mod eficient utilizand aceasta tehnica de reantrenare adaptiva combinata cu arhitectura in doua trepte a sistemului.
Cuvinte cheie: predictia curbelor de sarcinaretele neurale artificialeanaliza componentelor principalereantrenare
Abstract
The paper presents a system using an artificial neural network (ANN) to efficiently learn the shape of a non stationary sequence. The system has the potential to learn, classify and predict any stationary or non stationary signals, in particular the chronological load curves of power systems, at various time scales. A large variety of classification and forecasting applications to non stationary sequences can be efficiently solved by using this adaptive retraining technique combined with the two step system architecture.
Keywords: load forecastingArtificial Neural NetworksPrincipal Component Analysisretraining technique
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