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
LONGO, MICHELA;ZANINELLI, DARIO;
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.File in questo prodotto:
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