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The Research and Application of Ta Bei Power Network Load Forecasting

Author: YuFeiFei
Tutor: LiHong
School: Daqing Petroleum Institute
Course: Communication and Information System
Keywords: Power system Short-term load forecasting Neural Network Mallat algorithm GUI
CLC: TM715
Type: Master's thesis
Year: 2009
Downloads: 37
Quote: 0
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Abstract


Short-term power load forecasting is an important element in the power system . The accuracy of the load forecasting even more safe and economic operation of the power system has a very important role . So that the results of the short - term load forecasting higher demand. Tabei area power system load data using the method of combining wavelet transform and neural network for short-term load forecasting , and achieved some results . This paper analyzes the status quo of the current short-term power load forecasting , various forecasting methods and forecasting model , the conventional algorithm can better reflect the impact of weather conditions and other factors on the load , the artificial neural network with a high degree of non-linear mapping ability to consider the impact of these factors on the grid load , so this paper, the BP ( back Propagation ) network error return . Taking into account the BP network method of learning the speed of convergence , convergence stability , convergence to the global minimum value , there are still some unavoidable shortcomings , this paper presents the Mallat algorithm of wavelet theory is introduced into the BP network , Mallat algorithm load sequence wavelet decomposition, neural network model is constructed according to the characteristics of each component , and then the final prediction results obtained by wavelet reconstruction of each component forecast data , in order to better solve the above problems . Finally , using the GUI ( Graphical User Interface , Graphical User Interface) design interface makes operation easier . Based on the Tabei area of Xinjiang power load data prediction results show that , compared with traditional ANN (Artificial Neural Network, Artificial Neural Networks ) prediction method , the proposed model has higher prediction accuracy and strong adaptability has good application prospects .

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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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