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Title: Load forecasting in power system
Authors: Lee, Yunfeng
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
Issue Date: 2014
Abstract: Load forecasting had been a focal point of research throughout many countries. It played a vital role in the electrical industry such as economic dispatch, planning and operation of electrical utilities, energy transfer scheduling and many more. Thus, an accurate load forecasting would enable a correct anticipation of power needed to supply the demand. In order to achieve that, Support Vector Regression (SVR) model, hybridizing with Empirical Mode Decomposition (EMD), Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Complete Ensemble Empirical Mode Decomposition Adaptive Noise (CEEMDAN) methods were compared with 6 other models to determine which model would give the best performance. The load data of the New South Wales (Australia) would be used for our research in this paper. Despite inconclusive results in terms of the best model, the results proved that CEEMDAN method had enabled the improvement of load forecasting performance.
Schools: School of Electrical and Electronic Engineering 
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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