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.
|Titolo:||Evaluating innovative FCN Networks for energy prices' forecasting|
|Data di pubblicazione:||2016|
|Appare nelle tipologie:||4.1.2 Proceedings con ISBN|