Name
Abstract | ||
|---|---|---|
Load forecasting is very important for electrical companies since it permits the system operator, the planning, the distribution and the control of the electrical energy supplied to customers. Electric load is affected by many uncertain factors, so many factors should be considered in forecasting process. This paper introduced a new method based on data mining to reflect the influence of weather factor on load. This method constructed model by decision tree and used it to make short-term forecasting. During the construction of decision tree, test attributes were sorted by the principle of maximum information plus which can reduce the complexity of decision tree. Statistical analysis of history data of Tianjin indicates that this method can improve the precision of forecasting and is effective and practical. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/FSKD.2008.497 | FSKD (2) |
Keywords | DocType | Citations |
data mining,electric load,electric short-term load forecasting,electrical energy,statistical analysis,maximum information,short-term forecasting,decision tree,history data,electrical company,new method,load forecasting,decision trees,forecasting,meteorology | Conference | 2 |
PageRank | References | Authors |
0.54 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian-qiang Li | 1 | 7 | 1.83 |
| Cheng-lin Niu | 2 | 7 | 1.83 |
| Jizhen Liu | 3 | 60 | 17.03 |
| Junjie Gu | 4 | 53 | 6.59 |