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