Name
Title | ||
|---|---|---|
Energy Load Forecasting Using Empirical Mode Decomposition and Support Vector Regression |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TSG.2012.2235089 | Smart Grid, IEEE Transactions |
Keywords | Field | DocType |
load forecasting,power consumption,power engineering computing,power system stability,regression analysis,support vector machines,time series,empirical mode decomposition method,energy consumption prediction,energy consumption value oscillations,energy load forecasting,fuel supply planning,long-term load forecasting problem,office building dataset,operative condition scheduling,public-domain,short-term framework,support vector regression model,time series,Empirical mode decomposition,load forecasting,support vector regression | Scheduling (computing),Regression analysis,Load forecasting,Control engineering,Artificial intelligence,Mathematical optimization,Support vector machine,Exploit,Probabilistic forecasting,Engineering,Energy consumption,Machine learning,Hilbert–Huang transform | Journal |
Volume | Issue | ISSN |
4 | 1 | 1949-3053 |
Citations | PageRank | References |
24 | 1.54 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Ghelardoni | 1 | 24 | 1.54 |
| Alessandro Ghio | 2 | 667 | 35.71 |
| Davide Anguita | 3 | 1001 | 70.58 |