کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
996025 936283 2010 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Application of chaotic ant swarm optimization in electric load forecasting
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی مهندسی انرژی و فناوری های برق
پیش نمایش صفحه اول مقاله
Application of chaotic ant swarm optimization in electric load forecasting
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy Policy - Volume 38, Issue 10, October 2010, Pages 5830–5839
نویسندگان
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