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Forecasting electricity consumption is an important index for system planning, operation and decision making. In order to improve the accuracy of the forecasting, we apply an integrated architecture to optimize the prediction. Based on an integration of two machine learning techniques: artificial fish swarm algorithm search approach based on test-sample error estimate criterion (AFSAS-TEE) and support...
This paper describes a short time electrical energy demand forecast system using two different techniques of artificial intelligence: recurrent artificial neural networks and support vector regression. A brief analysis of the demand over the electrical energy network connection points is also done.
Wavelet packet theory and support vector regression (SVR) were introduced into server load prediction. A novel prediction algorithm called wavelet packet-SVR was proposed. Firstly, the algorithm decomposed and reconstructed the load time series into several signal branches by wavelet packet analysis. Secondly, SVR prediction models were constructed respectively to these branches and finally their...
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