Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI: | http://purl.tuc.gr/dl/dias/7965DDD4-336F-49E9-8152-6A86924EA0C8 | ||
| Έτος | 2022 | ||
| Τύπος | Κεφάλαιο σε Βιβλίο | ||
| Άδεια Χρήσης |
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| Βιβλιογραφική Αναφορά | D. Kolokotsa, “Indoor environmental quality and energy consumption assessment and ANN predictions for an integrated internet-based energy management system towards a Zero-energy building,” in Smart Zero‐energy Buildings and Communities for Smart Grids, Engineering, Energy and Architecture Set, Wiley, 2022, vol. 9, pp. 115-150, doi: 10.1002/9781119902201.ch5. https://doi.org/10.1002/9781119902201.ch5 | ||
| Εμφανίζεται στις Συλλογές |
This chapter reveals the most appropriate energy management techniques for transforming an institutional building to a zero-energy building in the tropics. Universities' campuses and campus buildings can be viewed as small districts and microgrids. Therefore, the school of design and environment at the national university of Singapore intends to integrate all buildings under a common energy and indoor environmental quality management platform. A cross correlation is performed between the air temperature measured at the various points and the loads as well as air temperature. Various types of artificial neural network have been developed and tested for the prediction of power loads as well as weather conditions. The nonlinear autoregressive network with exogenous inputs is a recurrent dynamic network, with feedback connections enclosing several layers of the network. Simulation and application of artificial intelligence control techniques, has indicated that they have the potential to make significant energy savings in buildings.
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