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Wavelet Transform Noise Elimination and Its Application in City Heating Load Prediction
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In this paper, the real-time measuring data with noise undergo wavelet transformation. With the treated data and an internal time-delay Elman network, city heating supply predictive models are established and short-term real-time predictions are realized. The result indicates that selecting the proper level of decomposition to denoise measuring signals can eliminate high frequency noise disturbance, improve identification precision, shorten identification time and meet the demands of real-time identification.
Jiang, Y.; Jun, X.; Wei, B. (2006). Wavelet Transform Noise Elimination and Its Application in City Heating Load Prediction. Energy Systems Laboratory (http://esl.tamu.edu); Texas A&M University (http://www.tamu.edu). Available electronically from