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ID:
098631
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| Publication |
2010.
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| Summary/Abstract |
Support vector regression (SVR) had revealed strong potential in accurate electric load forecasting, particularly by employing effective evolutionary algorithms to determine suitable values of its three parameters. Based on previous research results, however, these employed evolutionary algorithms themselves have several drawbacks, such as converging prematurely, reaching slowly the global optimal solution, and trapping into a local optimum. This investigation presents an SVR-based electric load forecasting model that applied a novel algorithm, namely chaotic ant swarm optimization (CAS), to improve the forecasting performance by searching its suitable parameters combination. The proposed CAS combines with the chaotic behavior of single ant and self-organization behavior of ant colony in the foraging process to overcome premature local optimum. The empirical results indicate that the SVR model with CAS (SVRCAS) results in better forecasting performance than the other alternative methods, namely SVRCPSO (SVR with chaotic PSO), SVRCGA (SVR with chaotic GA), regression model, and ANN model.
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| 2 |
ID:
099125
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| 3 |
ID:
160601
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| Summary/Abstract |
A CURIOUS DOG first leapt out of the bushes. It sniffed everything around it. Then it ran on. There was its mistress. The high boots that in England are called Wellingtons. Khaki jodhpurs. A green oilcloth jacket with a brown velvet collar. And the woman herself was a blonde with a distinctive (if not characteristic) bump in her nose, clearly passed down from her Norman ancestors.
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