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Short-Term Load Forecasting Based on Fuzzy Clustering Analysis and Least Squares Support Vector Machines

Author: NingBo
Tutor: YaoLiXiao
School: Xi'an University of Technology
Course: Proceedings of the
Keywords: Power system Short-term load forecasting Load Characteristics Fuzzy Clustering Analysis Support Vector Machine (SVM) Least squares support vector machine ( LS - SVM )
CLC: TM715
Type: Master's thesis
Year: 2009
Downloads: 67
Quote: 0
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Abstract


Power system short - term load forecasting is a very important basic work of the power system , and reasonable arrangements related to power generation, transmission and power distribution . Accurate , reliable electricity short - term load forecasting for power system economic , safe and reliable operation of particular importance . With the power systems become more complex , especially with the gradual deepening of the electricity market competition mechanism , the short-term load forecasting is given a higher demand. Therefore , fast and accurate short-term load forecasting in power system operation , scheduling and control has a very important theoretical practical significance . In depth, detailed study of the existing prediction algorithm based on the advantages and disadvantages of the various algorithms , as well as the extent to which it applies , especially the historical characteristics of the data extraction , related factors and prediction model, prediction methods conducted in-depth research . For different load characteristics and presentation of the law , the fuzzy clustering analysis of the introduction of short - term load forecasting , short - term load forecasting based on the optimization of the FCM clustering analysis and LS-SVM method . In considering the variation of the electric load on a periodic basis , on a sample of the historical load optimization FCM analysis , the optimal training sample set to obtain a classification and prediction of the optimal mode of the load sample time , to enhance the participation model LS-SVM training load sample input - output data regularity , to protect the training samples in a higher degree of approximation to meet the same input - output function of the effective combination of optimization of the FCM clustering analysis and LS-SVM algorithm . While reducing training samples to improve the prediction of the LS-SVM model , and simulation results show the effectiveness of the proposed hybrid model . Finally, the results obtained in this paper, the combination method compared to the single LS-SVM model and the results of BP model algorithm proves that the method has high precision , and achieved satisfactory results .

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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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