Name
Abstract | ||
|---|---|---|
This paper proposes a hybrid machine learning model for electricity demand forecasting, based on Bayesian Clustering by Dynamics (BCD) and Support Vector Machine (SVM). In the proposed model, a BCD classifier is firstly applied to cluster the input data set into several subsets by the dynamics of load series in an unsupervised manner, and then, groups of 24 SVMs for the next day's electricity demand curve are used to fit the training data of each subset. In the numerical experiment, the proposed model has been trained and tested on the data of the historical load from New York City. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11893257_105 | ICONIP |
Keywords | Field | DocType |
new york city,training data,input data,load series,historical load,bayesian clustering,bcd classifier,forecasting electricity demand,hybrid machine,electricity demand curve,electricity demand forecasting,machine learning,demand forecasting,support vector machine | Mean absolute percentage error,Demand forecasting,Computer science,Support vector machine,Marginal likelihood,Artificial intelligence,Classifier (linguistics),Artificial neural network,Cluster analysis,Machine learning,Bayesian probability | Conference |
Volume | ISSN | ISBN |
4233 | 0302-9743 | 3-540-46481-6 |
Citations | PageRank | References |
1 | 0.67 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shu Fan | 1 | 3 | 1.46 |
| Chengxiong Mao | 2 | 19 | 11.90 |
| Jiadong Zhang | 3 | 1 | 0.67 |
| Luonan Chen | 4 | 1485 | 145.71 |