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Prediction of Market Demand Based on AdaBoost_BP Neural Network
Aiming at the disadvantages of prediction model of
single BP neural network, a prediction model was presented by
combining AdaBoost algorithm and BP neural network for
improving the forecasting accuracy of single BP neural network.
The ensemble BP network based on AdaBoost is used as
intelligent algorithm. Overcoming the instability of single BP
neural network, the proposed models can give more accurate and
stable prediction for the novel conditions. The main influence
factors for prediction of market demand of refrigerator are
analyzing detailed and used as the inputs of proposed prediction
model. The efficiency of the proposed prediction model was
tested by simulation of the market demand statistical data of a
refrigerator enterprise in China. The simulation results have
shown that the higher accuracy is expressed in this proposed
model, and it is applicable to practice.
2015-06-30
Wind Prediction Based on Improved BP Artificial Neural Network in Wind Farm
Wind power prediction is important to the operation
of power system with comparatively large mount of wind power.
It can relieve or avoid the disadvantageous impact of wind farm
on power systems. Because the traditional neural network may
fall into local convergence, so it will be effective to improve the
training algorithm to improve its convergence and accuracy of
prediction. In this paper, a model for wind speed prediction was
constructed based on adaptive learning rate of BP neural
network, the selected historical wind speed data of a certain time
were use as model inputs, so that we can predict the wind speed
of the same time in the future and its accuracy analysis. Research
shows that the improved BP neural network model can
effectively achieve the long-term wind speed prediction.
2015-06-30
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