Publication

Improving recurrent network load forecasting

Alain Germond
1995
Conference Papers

Abstract

We present a not fully connected recurrent network applied to the problem of load forecasting. Although many authors have pointed out that recurrent networks were able to model NARMAX processes, we present a constructing scheme for the MA part. In addition we present a modification of the learning step which improves learning convergence and the accuracy of the forecast. At last, the use of a continuous learning scheme and a robust learning scheme, which appeared to be necessary when using a MA part, enables us to reach a good precision of the forecast, compared to the accuracy of the model in use at the utility at present

Official source

https://infoscience.epfl.ch/entities/publication/4fcb716a-4c17-4ab6-ad16-06cd4e1d53c1

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Alain Germond
1995
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/4fcb716a-4c17-4ab6-ad16-06cd4e1d53c1

About this result

Related concepts (27)

Recurrent neural network

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

Types of artificial neural networks

There are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.

Hopfield network

A Hopfield network (or Amari-Hopfield network, Ising model of a neural network or Ising–Lenz–Little model) is a form of recurrent artificial neural network and a type of spin glass system popularised by John Hopfield in 1982 as described by Shun'ichi Amari in 1972 and by Little in 1974 based on Ernst Ising's work with Wilhelm Lenz on the Ising model. Hopfield networks serve as content-addressable ("associative") memory systems with binary threshold nodes, or with continuous variables.

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