Benchmarking building energy performance: Accuracy by involving occupants in collecting data - A case study in Germany. (15th December 2022)

• Record Type: Journal Article
• Authors: Wederhake, Lars; Wenninger, Simon; Wiethe, Christian; Fridgen, Gilbert; Stirnweiß, Dominic
• Is Part Of: Journal of cleaner production. Volume 379:Part 2(2022)
• Publication Date: 2022-12-15
• Subjects: Energy quantification methods -- Energy performance certificate -- Data-driven methods -- Building data collection -- Building energy performance -- Energy efficiency; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2022.134762
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Energy performance certificates (EPC) aim to provide transparency about building energy performance (BEP) and benchmark buildings. Despite having qualified auditors examining buildings through on-site visits, BEP accuracy in EPCs is frequently criticized. Qualified auditors are often bound to engineering-based energy quantification methods. However, recent studies have revealed data-driven methods to be more accurate regarding benchmarking. Unlike engineering methods, data-driven methods can learn from data that non-experts might collect. This raises the question of whether data-driven methods allow for simplified data collection while still achieving the same accuracy as prescribed engineering-based methods. This study presents a method for selecting building variables, which even occupants can reliably collect and which at the same time contribute most to a data-driven method's predictive power. The method is tested and validated in a case study on a real-world data set containing 25, 000 German single-family houses. Having all data collected by non-experts, results show that the data-driven method achieves about 35% higher accuracy than the currently used engineering method by qualified auditors. Our study proposes a stepwise method to design data-driven EPCs, outlines design recommendations, and derives policy implications.