Name
Abstract | ||
|---|---|---|
Electric load forecasting is essential to improve the reliability of the ac power line data network and provide optimal load scheduling in an intelligent home system. In this paper, a short-term load forecasting realized by a neural fuzzy network (NFN) and a modified genetic algorithm (GA) is proposed. It can forecast the hourly load accurately with respect to different day types and weather information. By introducing new genetic operators, the modified GA performs better than the traditional GA under some benchmark test functions. The optimal network structure can be found by the modified GA when switches in the links of the network are introduced. The membership functions and the number of rules of the NFN can be obtained automatically. Results for a short-term load forecasting will be given. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/TIE.2003.819572 | IEEE Transactions on Industrial Electronics |
Keywords | Field | DocType |
Load forecasting,Fuzzy neural networks,Power system reliability,Weather forecasting,Intelligent systems,Intelligent networks,Genetic algorithms,Performance evaluation,Benchmark testing,Switches | Electrical load,Control theory,Computer science,Fuzzy logic,Load forecasting,AC power,Operator (computer programming),Load scheduling,Genetic algorithm,Network structure | Journal |
Volume | Issue | ISSN |
50 | 6 | 0278-0046 |
Citations | PageRank | References |
20 | 2.14 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. H. Ling | 1 | 609 | 40.29 |
| F. H. F. Leung | 2 | 616 | 33.93 |
| H. K. Lam | 3 | 3618 | 193.15 |
| P. K. S. Tam | 4 | 135 | 10.66 |