Identifying temporal properties of building components and indoor environment for building performance assessment. (15th January 2020)

• Record Type: Journal Article
• Authors: Deb, Chirag; Frei, Mario; Schlueter, Arno
• Is Part Of: Building and environment. Volume 168(2020)
• Publication Date: 2020-01-15
• Subjects: Temporal dynamics -- Building properties -- Wireless sensor network -- Time lag -- Data analytics -- Performance gap -- Energy saving; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2019.106506
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: Buildings are dynamic thermal systems that require energy to maintain a comfortable indoor environment. Knowing these dynamics allows for identifying relevant building characteristics for assessing building energy performance. However, conventional building simulation programs take daily or monthly mean values for performance assessment. These often aggregate the errors and uncertainties between the theoretical and the actual building performance while missing the temporal dynamics on a minute or hourly basis. The objective of this study is to uncover the temporal aspects of building properties using data obtained by a wireless sensor network (WSN). Our analysis focuses on the following seven variables that are relevant for assessing building performance and retrofit measures: indoor air temperature, window opening instances, CO2 concentration, supply temperature from the heating system, outdoor air temperature, heat flux through the wall and the window. Using a single-family residence as a case study, we identify the temporal dynamics of these variables using polar plots on a high-resolution, 5-minute interval dataset. Following this, we define a set of conditional rules to study 'expected' and 'unexpected' impact of the six variables on the indoor air temperature. We observe strong temporal dynamics for certain building components, resulting in a large time lag. The conditional rule analysis also allows identifying energy saving potentials and factors contributing to the performance gap. For example, we observe high indoor air temperatures and at times when no occupants are present. Finally, we discuss the benefits of this approach with respect to building retrofit and energy performance gap analysis. Highlights: Use of a lean WSN to acquire high-resolution building and indoor environmental data. Analysis identifies temporal aspects of building properties and occupant behavior. Conditional rules study the mutual variation of variables with indoor air temperature. Strong temporal dynamics and varying properties of building elements is revealed.