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A low-complexity non-intrusive approach to predict the energy demand of buildings over short-term horizons

Panagopoulos Athanasios Aris, Christianos Filippos, Katsigiannis Michail, Mykoniatis Konstantinos, Pritoni Marco, Panagopoulos Orestis P., Peffer Therese, Chalkiadakis Georgios, Culler David E., Jennings Nicholas R., Lipman Timothy

Απλή Εγγραφή

URIhttp://purl.tuc.gr/dl/dias/392F0185-103C-484C-80AA-EE90020DFE92-
Αναγνωριστικόhttps://doi.org/10.1080/17512549.2020.1835712-
Αναγνωριστικόhttps://www.tandfonline.com/doi/full/10.1080/17512549.2020.1835712-
Γλώσσαen-
Μέγεθος12 pagesen
ΤίτλοςA low-complexity non-intrusive approach to predict the energy demand of buildings over short-term horizonsen
ΔημιουργόςPanagopoulos Athanasios Arisen
ΔημιουργόςChristianos Filipposen
ΔημιουργόςKatsigiannis Michailen
ΔημιουργόςMykoniatis Konstantinosen
ΔημιουργόςPritoni Marcoen
ΔημιουργόςPanagopoulos Orestis P.en
ΔημιουργόςPeffer Thereseen
ΔημιουργόςChalkiadakis Georgiosen
ΔημιουργόςΧαλκιαδακης Γεωργιοςel
ΔημιουργόςCuller David E.en
ΔημιουργόςJennings Nicholas R.en
ΔημιουργόςLipman Timothyen
ΕκδότηςTaylor and Francisen
ΠερίληψηReliable, 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
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2022-07-25-
Ημερομηνία Δημοσίευσης2022-
Θεματική ΚατηγορίαEnergy demanden
Θεματική ΚατηγορίαEnergy consumptionen
Θεματική ΚατηγορίαForecastingen
Θεματική ΚατηγορίαSmart buildingsen
Βιβλιογραφική ΑναφοράA. 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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