Improved GART Neural Network Model for Pattern Classification and Rule Extraction With Application to Power Systems

2011 ◽  
Vol 22 (12) ◽  
pp. 2310-2323 ◽  
Author(s):  
Keem Siah Yap ◽  
Chee Peng Lim ◽  
Mau Teng Au
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Network Model ◽  
Pattern Classification ◽  
Neural Network Model ◽  
Rule Extraction

A neural network model with bounded-weights for pattern classification

2004 ◽  
Vol 31 (9) ◽  
pp. 1411-1426 ◽  
Author(s):  
Yi Liao ◽  
Shu-Cherng Fang ◽  
Henry L.W. Nuttle
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Pattern Classification ◽  
Neural Network Model

scholarly journals Forecasting Error Modelling for Improving PV Generation Prediction

2019 ◽  
Vol 2 (1) ◽  
pp. 27-36
Author(s):  
Happy Aprillia ◽  
Hong-Tzer Yang
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Network Model ◽  
Neural Network Model ◽  
Weather Condition ◽  
Absolute Error ◽  
Density Estimator ◽  
Forecasting Error ◽  
Uncertainty Model ◽  
Pv Generation

Accurate forecasting of Photovoltaic (PV) generation output is important in operation of high PV-penetrated power systems. In this paper, an adaptive uncertainty modelling method for forecasting error is proposed to improve the prediction accuracy of PV generation. The proposed method models the uncertainty in forecast data using Kernel Density Estimator and guarantee the provision of accurate expected value. Neural Network model is then constructed by the developed uncertainty model to forecast the PV output. The actual confidence level is traced within the day and injected as an input to the Neural Network model by observing the Mean Absolute Prediction Error (MAPE) and Unscaled Mean Bounded Relative Absolute Error (UMBRAE). The proposed method is tested with various significant changes of weather condition and proved to have promising performance on PV generation forecasting. Thus, the developed adaptive uncertainty model can be further used in power system planning that have high-penetration energy sources with stochastic behavior.

Application of Forecasting Power Based on Neural Network and Wilcoxon Rank Sum Test

2014 ◽  
Vol 1046 ◽  
pp. 560-563
Author(s):  
Zhi Peng Tian ◽  
Chang Hao Xia ◽  
Jian Ping Chen
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Network Model ◽  
Neural Network Model ◽  
Load Forecasting ◽  
Weather Conditions ◽  
Short Term ◽  
Accuracy Requirement ◽  
Wilcoxon Rank Sum Test ◽  
Power Load

With the development of modern power systems, the accuracy requirement for load forecasting is getting higher and higher. In this paper, the Wilcoxon rank sum test as a new mathematical method to test error, is applied to the neural network based short-term load forecasting model. The actual historical load data and the associated weather conditions factors to be considered, based on MATLAB neural network toolbox, The method to construct three different neural network model, the power load in Yichang area in 2009 was forecasted and the simulation of short-term. The simulation results show：Wilcoxon rank sum test algorithms in neural network model, not only can correctly predict，relative to the average relative error, in the statistical methods and statistical laws - especially in the face of large amounts of data - through a random sample to show a certain advantage in the overall distribution.

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