NDLI: Techniques for Training Sets Selection in the Representation of a Thermosiphon System Via ANN

Content Provider	IEEE Xplore Digital Library
Author	Zarate, L.E. Pereira, E.M.D. Oliveira, L.A.R. Gil, V.P. Santos, T.R.A. Nogueira, B.M.
Copyright Year	2006
Description	Author affiliation: Pontifical Catholic Univ. of Minas Gerais, Belo Horizonte (Zarate, L.E.; Oliveira, L.A.R.; Gil, V.P.; Santos, T.R.A.; Nogueira, B.M.)
Abstract	The research of alternative forms of energy production became more important nowadays in a context where the natural resources are scarce. In this sense, thermosiphon systems have been developed as an alternative way of energy economy for the water heating process using a renewable energy source: the sun. A thermosiphon system is greatly influenced by several parameters: the ambient temperature, the input water temperature, the solar irradiance, the flow rate, the inclination of the solar collector, the height of the water storage tank and mainly by the manufacturing process. Nowadays, there are interests in the development of analytical models that consider parameters of installation such as: height of the water storage tank and inclination of the solar collector. These analytical models can be complex and nonlinear. In the last decades, ANN's (i.e. artificial neural networks) have been used to represent many kinds of industrial processes, dealing with the complexity and non-linearity of them. Moreover, ANN's are capable of dealing with aspects of manufacturing not considered by the analytical models but that are important in determining the efficiency of the real thermosiphon system. A trained ANN can eliminate the necessity of new laboratory experiments for real and new conditions of installation. A better modeling of the process by means of ANN depends on a representative training set. In order to define the training set better, statistical ways and clustering techniques have been proposed and compared. Results of both techniques have been discussed in this work.
Starting Page	2736
Ending Page	2741
File Size	221968
Page Count	6
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.247178
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Water storage Analytical models Water heating Water resources Temperature sensors Production Renewable energy resources Sun Manufacturing processes
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in