A low-complexity non-intrusive approach to predict the energy demand of buildings over short-term horizons. Issue 2 (4th March 2022)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Advances in building energy research. Volume 16:Issue 2(2022)
• Page Start: 202
• Page End: 213
• Publication Date: 2022-03-04
• Subjects: Energy demand -- energy consumption -- forecasting -- smart buildings; Sustainable buildings -- Design and construction -- Periodicals; Architecture and energy conservation -- Periodicals; Buildings -- Energy conservation -- Periodicals; 696
• DOI: 10.1080/17512549.2020.1835712
• Languages: English
• ISSNs: 1751-2549

Abstract

ABSTRACT: 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.