CINECA IRIS Institutional Research Information System
IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.
EnglishSeveral techniques can be retrieved in literature, which cope with the problem of energy load forecasting in the short-term framework, that is hours up to few days ahead. However, in order to properly schedule operative conditions including e- nergy purchasing and generation, fuel supply, and infrastruc- ture development and maintenance, a long-term prediction is of crucial importance. In this framework, the generaliza- tion of the short-term techniques to this more complex prob- lem is usually characterized by a scarce performance. In this paper, we present an innovative method, which exploits the Savitzky-Golay filter and the Support Vector Regression al- gorithm in order to reliably predict the energy consumption in the long-term framework: the extrapolation ability of our proposal is validated on a real-world problem.
Scheda prodotto non validato
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo
| Titolo: | Long-Term Energy Load Forecasting Using Auto-Regressive and Approximating Support Vector Regression |
| Autori: | |
| Data di pubblicazione: | 2012 |
| Abstract: | Several techniques can be retrieved in literature, which cope with the problem of energy load forecasting in the short-term framework, that is hours up to few days ahead. However, in order to properly schedule operative conditions including e- nergy purchasing and generation, fuel supply, and infrastruc- ture development and maintenance, a long-term prediction is of crucial importance. In this framework, the generaliza- tion of the short-term techniques to this more complex prob- lem is usually characterized by a scarce performance. In this paper, we present an innovative method, which exploits the Savitzky-Golay filter and the Support Vector Regression al- gorithm in order to reliably predict the energy consumption in the long-term framework: the extrapolation ability of our proposal is validated on a real-world problem. |
| Handle: | http://hdl.handle.net/11567/628600 |
| ISBN: | 9781467314541 |
| Appare nelle tipologie: | 04.01 - Contributo in atti di convegno |
File in questo prodotto:
Copyright © 2021