Name
Title | ||
|---|---|---|
Electricity Load Prediction using Fuzzy c-means Clustering EMD based Support Vector Regression for University Building |
Abstract | ||
|---|---|---|
Improving energy efficiency is crucial since it helps to bring benefit to the environment as well as cost savings. An accurate electricity load prediction offers opportunities to building manager for having a better planning of their building operational strategies, and increase its efficiency. Electricity load prediction itself has become a challenging task due to its complex and non-stationary pattern. This study proposes a hybrid algorithm that combines fuzzy c-means clustering approach, empirical mode decomposition, and support vector regression to predict building electricity load in the university. Experimental results demonstrated that the proposed hybrid method could improve SVR prediction accuracy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/iFUZZY46984.2019.9066226 | 2019 International Conference on Fuzzy Theory and Its Applications (iFUZZY) |
Keywords | DocType | ISSN |
Energy,time series,prediction,data mining | Conference | 2377-5823 |
ISBN | Citations | PageRank |
978-1-7281-0841-4 | 0 | 0.34 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Irene Karijadi | 1 | 0 | 0.68 |
| Shuo-Yan Chou | 2 | 537 | 48.50 |
| Anindhita Dewabharata | 3 | 2 | 1.42 |
| Ray-Guang Cheng | 4 | 330 | 34.73 |