Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/392F0185-103C-484C-80AA-EE90020DFE92 | - |
| Αναγνωριστικό | https://doi.org/10.1080/17512549.2020.1835712 | - |
| Αναγνωριστικό | https://www.tandfonline.com/doi/full/10.1080/17512549.2020.1835712 | - |
| Γλώσσα | en | - |
| Μέγεθος | 12 pages | en |
| Τίτλος | A low-complexity non-intrusive approach to predict the energy demand of buildings over short-term horizons | en |
| Δημιουργός | Panagopoulos Athanasios Aris | en |
| Δημιουργός | Christianos Filippos | en |
| Δημιουργός | Katsigiannis Michail | en |
| Δημιουργός | Mykoniatis Konstantinos | en |
| Δημιουργός | Pritoni Marco | en |
| Δημιουργός | Panagopoulos Orestis P. | en |
| Δημιουργός | Peffer Therese | en |
| Δημιουργός | Chalkiadakis Georgios | en |
| Δημιουργός | Χαλκιαδακης Γεωργιος | el |
| Δημιουργός | Culler David E. | en |
| Δημιουργός | Jennings Nicholas R. | en |
| Δημιουργός | Lipman Timothy | en |
| Εκδότης | Taylor and Francis | en |
| Περίληψη | Reliable, non-intrusive, short-term (of up to 12 h ahead) prediction of a building's energy demand is a critical component of intelligent energy management applications. A number of such approaches have been proposed over time, utilizing various statistical and, more recently, machine learning techniques, such as decision trees, neural networks and support vector machines. Importantly, all of these works barely outperform simple seasonal auto-regressive integrated moving average models, while their complexity is significantly higher. In this work, we propose a novel low-complexity non-intrusive approach that improves the predictive accuracy of the state-of-the-art by up to ∼10%. The backbone of our approach is a K-nearest neighbours search method, that exploits the demand pattern of the most similar historical days, and incorporates appropriate time-series pre-processing and easing. In the context of this work, we evaluate our approach against state-of-the-art methods and provide insights on their performance. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2022-07-25 | - |
| Ημερομηνία Δημοσίευσης | 2022 | - |
| Θεματική Κατηγορία | Energy demand | en |
| Θεματική Κατηγορία | Energy consumption | en |
| Θεματική Κατηγορία | Forecasting | en |
| Θεματική Κατηγορία | Smart buildings | en |
| Βιβλιογραφική Αναφορά | A. A. Panagopoulos, F. Christianos, M. Katsigiannis, K. Mykoniatis, M. Pritoni, O. P. Panagopoulos, T. Peffer, G. Chalkiadakis, D. E. Culler, N. R. Jennings, and T. Lipman, “A low-complexity non-intrusive approach to predict the energy demand of buildings over short-term horizons,” Adv. Build. Energy Res., vol. 16, no. 2, pp. 202–213, Mar. 2022, doi: 10.1080/17512549.2020.1835712. | en |
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