A low-complexity non-intrusive approach to predict the energy demand of buildings over short-term horizonsA low-complexity non-intrusive approach to predict the energy demand of buildings over short-term horizons
Peer-Reviewed Journal Publication
Δημοσίευση σε Περιοδικό με Κριτές
2022-07-252022enReliable, 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.http://creativecommons.org/licenses/by/4.0/Advances in Building Energy Research162202-213
Panagopoulos Athanasios Aris
Christianos Filippos
Katsigiannis Michail
Mykoniatis Konstantinos
Pritoni Marco
Panagopoulos Orestis P.
Peffer Therese
Chalkiadakis Georgios
Χαλκιαδακης Γεωργιος
Culler David E.
Jennings Nicholas R.
Lipman Timothy
Taylor and Francis
Energy demand
Energy consumption
Forecasting
Smart buildings