Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/392F0185-103C-484C-80AA-EE90020DFE92 | - |
| Identifier | https://doi.org/10.1080/17512549.2020.1835712 | - |
| Identifier | https://www.tandfonline.com/doi/full/10.1080/17512549.2020.1835712 | - |
| Language | en | - |
| Extent | 12 pages | en |
| Title | A low-complexity non-intrusive approach to predict the energy demand of buildings over short-term horizons | en |
| Creator | Panagopoulos Athanasios Aris | en |
| Creator | Christianos Filippos | en |
| Creator | Katsigiannis Michail | en |
| Creator | Mykoniatis Konstantinos | en |
| Creator | Pritoni Marco | en |
| Creator | Panagopoulos Orestis P. | en |
| Creator | Peffer Therese | en |
| Creator | Chalkiadakis Georgios | en |
| Creator | Χαλκιαδακης Γεωργιος | el |
| Creator | Culler David E. | en |
| Creator | Jennings Nicholas R. | en |
| Creator | Lipman Timothy | en |
| Publisher | Taylor and Francis | en |
| Content Summary | 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 |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2022-07-25 | - |
| Date of Publication | 2022 | - |
| Subject | Energy demand | en |
| Subject | Energy consumption | en |
| Subject | Forecasting | en |
| Subject | Smart buildings | en |
| Bibliographic Citation | 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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