Forecasting electricity prices is today an essential tool in the day-ahead competitive market. Prediction techniques based on neural and fuzzy neural networks are very promising in terms of prediction performance and model accuracy. In this paper, we investigate the applicability to the electricity market of three well-known approaches, namely Radial Basis Function neural networks, Mixture of Gaussian neural networks and Higher-Order Neuro-Fuzzy Inference System. Through a set of real-world examples we assess the applicability of such methodologies for medium-term energy price projections.
Neural network approaches to electricity price forecasting in day-ahead markets / Rosato, Antonello; Altilio, Rosa; Araneo, Rodolfo; Panella, Massimo. - (2018), pp. 1-5. (Intervento presentato al convegno IEEE International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems Europe (IEEE EEEIC / I&CPS Europe 2018) tenutosi a Palermo, Italia) [10.1109/EEEIC.2018.8493837].
Neural network approaches to electricity price forecasting in day-ahead markets
Rosato, Antonello;Altilio, Rosa;Araneo, Rodolfo;Panella, Massimo
2018
Abstract
Forecasting electricity prices is today an essential tool in the day-ahead competitive market. Prediction techniques based on neural and fuzzy neural networks are very promising in terms of prediction performance and model accuracy. In this paper, we investigate the applicability to the electricity market of three well-known approaches, namely Radial Basis Function neural networks, Mixture of Gaussian neural networks and Higher-Order Neuro-Fuzzy Inference System. Through a set of real-world examples we assess the applicability of such methodologies for medium-term energy price projections.File | Dimensione | Formato | |
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