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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

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URIhttp://purl.tuc.gr/dl/dias/392F0185-103C-484C-80AA-EE90020DFE92-
Identifierhttps://doi.org/10.1080/17512549.2020.1835712-
Identifierhttps://www.tandfonline.com/doi/full/10.1080/17512549.2020.1835712-
Languageen-
Extent12 pagesen
TitleA low-complexity non-intrusive approach to predict the energy demand of buildings over short-term horizonsen
CreatorPanagopoulos Athanasios Arisen
CreatorChristianos Filipposen
CreatorKatsigiannis Michailen
CreatorMykoniatis Konstantinosen
CreatorPritoni Marcoen
CreatorPanagopoulos Orestis P.en
CreatorPeffer Thereseen
CreatorChalkiadakis Georgiosen
CreatorΧαλκιαδακης Γεωργιοςel
CreatorCuller David E.en
CreatorJennings Nicholas R.en
CreatorLipman Timothyen
PublisherTaylor and Francisen
Content SummaryReliable, 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
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2022-07-25-
Date of Publication2022-
SubjectEnergy demanden
SubjectEnergy consumptionen
SubjectForecastingen
SubjectSmart buildingsen
Bibliographic CitationA. 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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