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.
EnglishIn this paper we focus our attention on the long-term load forecasting problem, that is the prediction of energy consump- tion for several months ahead (up to one or more years), useful in order to ease the proper scheduling of operative conditions (such as the planning of fuel supply). While several effective techniques are available in the short-term framework, no reliable methods have been proposed for long-term predictions. For this purpose, we de- scribe in this work a new procedure, which exploits the Empirical Mode Decomposition method to disaggregate a time series into two sets of components, respectively describing the trend and the local oscillations of the energy consumption values. These sets are then used for training Support Vector Regression models. The experi- mental results, obtained both on a public-domain and on an office building dataset, allow to validate the effectiveness of the proposed method.
Scheda prodotto non validato
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo
| Titolo: | Energy Load Forecasting Using Empirical Mode Decomposition and Support Vector Regression |
| Autori: | |
| Data di pubblicazione: | 2013 |
| Rivista: | |
| Abstract: | In this paper we focus our attention on the long-term load forecasting problem, that is the prediction of energy consump- tion for several months ahead (up to one or more years), useful in order to ease the proper scheduling of operative conditions (such as the planning of fuel supply). While several effective techniques are available in the short-term framework, no reliable methods have been proposed for long-term predictions. For this purpose, we de- scribe in this work a new procedure, which exploits the Empirical Mode Decomposition method to disaggregate a time series into two sets of components, respectively describing the trend and the local oscillations of the energy consumption values. These sets are then used for training Support Vector Regression models. The experi- mental results, obtained both on a public-domain and on an office building dataset, allow to validate the effectiveness of the proposed method. |
| Handle: | http://hdl.handle.net/11567/628598 |
| Appare nelle tipologie: | 01.01 - Articolo su rivista |
File in questo prodotto:
http://hdl.handle.net/11567/628598
Copyright © 2021