Wavelet Transform Noise Elimination and Its Application in City Heating Load Prediction

dc.creator	Jiang, Y.
dc.creator	Jun, X.
dc.creator	Wei, B.
dc.date.accessioned	2007-05-07T20:42:49Z
dc.date.available	2007-05-07T20:42:49Z
dc.date.issued	2006
dc.identifier.other	ESL-IC-06-11-106
dc.identifier.uri	https://hdl.handle.net/1969.1/5245
dc.description.abstract	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.	en
dc.format.extent	123649 bytes	en
dc.format.mimetype	application/pdf
dc.language.iso	en_US
dc.publisher	Energy Systems Laboratory (http://esl.tamu.edu)
dc.publisher	Texas A&M University (http://www.tamu.edu)
dc.subject	wavelet transform	en
dc.subject	data denoising	en
dc.subject	Elman network	en
dc.subject	heat load prediction	en
dc.title	Wavelet Transform Noise Elimination and Its Application in City Heating Load Prediction	en