Name
Abstract | ||
|---|---|---|
In this paper, an integrated analytics model is applied to power consumption in Taiwan. The data mining algorithms, K-mean and CART (classification and regression tree) are first used for generating cluster, decision tree, and decision rules in this proposed approach. Furthermore, the multiple regression analysis is applied to predict power consumption. According to simulation results, the power consumption data is divided into 12 clusters (groups). The decision tree and 11 rules are obtained from CART. Finally, a statistical significance regression equation is obtained for the prediction of power consumption. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICACI.2018.8377486 | 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI) |
Keywords | Field | DocType |
low voltage meter,data mining,K-mean,CART,regression,power consumption | Decision rule,Decision tree,Data mining,Regression analysis,Cart,Computer science,Power demand,Data mining algorithm,Analytics,Power consumption | Conference |
ISBN | Citations | PageRank |
978-1-5386-4363-1 | 0 | 0.34 |
References | Authors | |
1 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin-Yu Peng | 1 | 0 | 1.01 |
| So-Tsung Chou | 2 | 1 | 1.38 |
| Chou-Yuan Lee | 3 | 295 | 17.36 |
| Kuo-Chung Chu | 4 | 32 | 10.15 |
| Sano Natsuki | 5 | 0 | 0.34 |
| Zne-Jung Lee | 6 | 940 | 43.45 |