Παρακαλώ χρησιμοποιήστε αυτό το αναγνωριστικό για να παραπέμψετε ή να δημιουργήσετε σύνδεσμο προς αυτό το τεκμήριο:
https://hdl.handle.net/20.500.14279/13369
Τίτλος: | Implementing artificial neural networks in energy building applications - A review | Συγγραφείς: | Georgiou, Georgios C. Christodoulides, Paul Kalogirou, Soteris A. |
Major Field of Science: | Engineering and Technology | Field Category: | Electrical Engineering - Electronic Engineering - Information Engineering;Environmental Engineering | Λέξεις-κλειδιά: | Artificial Intelligence;Artificial Neural Networks;Buildings;Energy;Renewable Energy | Ημερομηνία Έκδοσης: | Ιου-2018 | Πηγή: | 5th IEEE International Energy Conference, 2018, 3-7 June, Limassol, Cyprus | Conference: | 5th IEEE International Energy Conference | Περίληψη: | Artificial Neural Networks (ANNs) constitute a research area of high interest, for both practitioners and academics, as they are found very useful for solving complex problems that are difficult to solve using known and well developed conventional methods or techniques. They can be used for prediction, control, estimation, data clustering and many other applications that are found in everyday scenarios. This paper explains in brief the basic theory of ANNs, followed by a review of different studies related to ANNs used for applications in buildings such as energy management, systems control and energy prediction. It has been found that applying ANNs in buildings the energy consumption can be reduced, depending on the application. Furthermore, efficient control mechanisms also become possible, leading to the reduction of the energy consumption. Through this review, the reader will be able to recognise the value of ANNs and their big potential in buildings and energy sector, in general. Finally, an ANN-based structure for predicting the local RES generation and the load demand for a building is proposed. | URI: | https://hdl.handle.net/20.500.14279/13369 | ISBN: | 978-1-5386-3669-5 | DOI: | 10.1109/ENERGYCON.2018.8398847 | Rights: | © 2018 IEEE. | Type: | Conference Papers | Affiliation: | Cyprus University of Technology | Publication Type: | Peer Reviewed |
Εμφανίζεται στις συλλογές: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
CORE Recommender
SCOPUSTM
Citations
1
13
checked on 6 Νοε 2023
Page view(s) 1
476
Last Week
9
9
Last month
29
29
checked on 14 Μαρ 2025
Google ScholarTM
Check
Altmetric
Όλα τα τεκμήρια του δικτυακού τόπου προστατεύονται από πνευματικά δικαιώματα