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The paper presents an improved method for 1-24 hours load forecasting in the power system, integrating and combining different neural forecasting results by an ensemble system. We will integrate the results of partial predictions made by three solutions, out of which one relies on a multilayer perceptron and two others on self-organizing networks of the competitive type. As the expert system we will...
The paper presents the application of an ensemble of neural predictors for forecasting the daily meteorological PM10 pollution. The support vector machine has been used as the basic predicting network. The bagging technique has been applied to adapt different predictors. The results of many predictors have been combined together to form final forecasting. The blind source separation has been applied...
The paper present the new method of accurate prognosis of the short term load pattern for 24 hours ahead. The method uses the ensemble of neural predictors combined together by applying the blind source separation approach. Thanks to this the less accurate prognoses are not rejected but used to improve the accuracy of the final forecast. The numerical results concerning the prediction of 24-hour pattern...
The paper presents and compares the performance of different prewhitening algorithms of the signals in the presence of white noise. The algorithms have been applied to the decorrelation of the statistically dependent and independent signals mixed together. The presented technigue may find application in the solutions of the blind source separation problems.
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