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Combination of power system short - term load forecasting principal component analysis and artificial neural networks

Author: SuZuoHong
Tutor: WuJunJi
School: Nanjing University of Technology and Engineering
Course: Proceedings of the
Keywords: Artificial Neural Networks BP algorithm Principal Component Analysis Parameter compression Combination Forecasting Cycle parameters Short - term load forecasting
CLC: TM714.1
Type: Master's thesis
Year: 2002
Downloads: 378
Quote: 10
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Abstract


The combined use of artificial neural network theory and other theories to solve practical problems is the research topic of concern in recent years . BP artificial neural network in parameter selection is still no existing theoretical basis to follow , before you use a combination of simulation modeling , made ??a thorough study of this article for the choice of these parameters , this part of the research work , including the artificial neural network input best compression range of the input parameters of the layer , the best learning parameters of the artificial neural network model , periodic input data best sample length . The results of these studies provide a good foundation for the next step of the combination of modeling and simulation methods . Multivariate principal component analysis theory is extracted with the relationship between the dimension of the random vector seeking covariance approach , is about to extract the main features of the data component of multi-dimensional vector . This paper this method combined with artificial neural network is used to forecast short-term load . Simulation results show that this new method is not only can improve the prediction efficiency , and set up the model has a good scalability , having good application feasibility .

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CLC: > Industrial Technology > Electrotechnical > Transmission and distribution engineering, power network and power system > Theory and Analysis > Load analysis > Load power factor to improve
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