In a competitive electricity market, an accurate forecasting of energy prices is an important activity for all the market participants. This paper proposes a novel approach based on Neural Networks for forecasting energy prices. Two different architectures of Neural Networks are used. In particular, Multi-Layer Perceptron (MLP) and Fully Connected Neural (FCN) networks are designed, calibrated and compared.

Evaluating innovative FCN Networks for energy prices' forecasting

SIANO, PIERLUIGI;
2016-01-01

Abstract

In a competitive electricity market, an accurate forecasting of energy prices is an important activity for all the market participants. This paper proposes a novel approach based on Neural Networks for forecasting energy prices. Two different architectures of Neural Networks are used. In particular, Multi-Layer Perceptron (MLP) and Fully Connected Neural (FCN) networks are designed, calibrated and compared.
2016
9781509020676
9781509020676
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4675776
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