TY - JOUR AU - Athanasios Aris Panagopoulos AU - Filippos Christianos AU - Michail Katsigiannis AU - Konstantinos Mykoniatis AU - Marco Pritoni AU - Orestis P Panagopoulos AU - Therese Peffer AU - Georgios Chalkiadakis AU - David Culler AU - Nicholas R Jennings AU - Timothy E Lipman AB -

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 preprocessing and easing. In the context of this work, we evaluate our approach against state-of-the-art methods and provide insights on their performance.

BT - Advances in Building Energy Research DA - 04/03/2022 DO - 10.1080/17512549.2020.1835712 IS - 2 N2 -

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 preprocessing and easing. In the context of this work, we evaluate our approach against state-of-the-art methods and provide insights on their performance.

PB - Informa UK Limited PY - 2022 SP - 202 EP - 213 T2 - Advances in Building Energy Research TI - A low-complexity non-intrusive approach to predict the energy demand of buildings over short-term horizons UR - https://doi.org/10.1080/17512549.2020.1835712 VL - 16 SN - 1751-2549, 1756-2201 ER -