By Topic

Energy Load Forecasting Using Empirical Mode Decomposition and Support Vector Regression

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Ghelardoni, L. ; DITEN, Univ. of Genoa, Genoa, Italy ; Ghio, A. ; Anguita, D.

In this paper we focus our attention on the long-term load forecasting problem, that is the prediction of energy consumption 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 describe 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 experimental results, obtained both on a public-domain and on an office building dataset, allow to validate the effectiveness of the proposed method.

Published in:

Smart Grid, IEEE Transactions on  (Volume:4 ,  Issue: 1 )