000176117 001__ 176117
000176117 005__ 20190316235333.0
000176117 037__ $$aARTICLE
000176117 245__ $$aNeurobat, a Predictive and Adaptive Heating Control System Using Artificial Neural Networks
000176117 269__ $$a2001
000176117 260__ $$c2001
000176117 336__ $$aJournal Articles
000176117 520__ $$aThe paper describes a predictive and adaptive heating controller, using artificial neural networks to allow the adaptation of the control model to the real conditions (climate, building characteristics, user's behaviour). The controller algorithm has been developed and tested as a collaborative project between the CSEM (Centre Suisse d'Electronique et de Microtechnique, Neuchâtel, Switzerland, project leader), and the LESO-PB (Solar Energy and Building Physics Laboratory, EPFL, Lausanne, Switzerland). A significant support has been provided by leading Swiss industries in control systems. The project itself has been funded by the Swiss Federal Office of Energy (SFOE). The project has allowed the development of an original algorithm, especially suited for water heating systems, and its testing both by simulation and by experimentation on an inhabited building. The experimentation has been done using a PC software implementation. A second phase of the project, currently going on, aims at building a commercial system based on the NEUROBAT algorithm.
000176117 6531_ $$aHeating Equipment, Self Commissioning, Adaptive Controller, Predictive Controller, Artificial Neural Networks (ANN), Fuzzy Logic
000176117 700__ $$0240621$$g105906$$aMorel, Nicolas
000176117 700__ $$aBauer, Manuel
000176117 700__ $$aEl-Khoury, Mario
000176117 700__ $$aKrauss, Jens
000176117 773__ $$j21$$tSolar Energy Journal$$q161-201
000176117 8564_ $$uhttps://infoscience.epfl.ch/record/176117/files/e_Int-Journal_Solar_Energy_May_2001_English.pdf$$zn/a$$s354814$$yn/a
000176117 909C0 $$xU10262$$0252072$$pLESO-PB
000176117 909CO $$ooai:infoscience.tind.io:176117$$qGLOBAL_SET$$particle$$pENAC
000176117 917Z8 $$x106442
000176117 937__ $$aEPFL-ARTICLE-176117
000176117 973__ $$rNON-REVIEWED$$sPUBLISHED$$aEPFL
000176117 980__ $$aARTICLE