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A Short-term Load Forecasting Based on Hierarchical Analysis of Meteorological Factors

Author: XuHongRui
Tutor: LiRan
School: North China Electric Power University (Hebei)
Course: Proceedings of the
Keywords: Self-organizing feature map neural network Empirical Mode Decomposition Spearman rank correlation coefficient Particle swarm optimization Support Vector Machine
CLC: TM715
Type: Master's thesis
Year: 2009
Downloads: 120
Quote: 0
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Abstract


Forecasting as a clue, do a thorough study of load clustering , hierarchical analysis of meteorological factors , predictive model . Load sequence cluster analysis with self-organizing feature map neural network , and then through the empirical mode decomposition method automatically load sequence within each cluster is broken down into a number of independent intrinsic mode components , each component of meteorological factors Sri Lanka the Pearman Related dig meteorological factors on each load component . According to the results of the hierarchical correlation analysis for each level of load components to build particle swarm optimization , support vector machine load forecasting models , combination of meteorological factors to predict layers component and summarize the results . Tangshan region case study shows that the method has higher prediction accuracy and stronger generalization ability .

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CLC: > Industrial Technology > Electrotechnical > Transmission and distribution engineering, power network and power system > Theory and Analysis > Power system planning
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