Comparing the impact of presence patterns on energy demand in residential buildings using measured data and simulation models

Abstract

Prediction of the energy performance of buildings helps designers with decision-making during the design process in new construction, as well as in renovation projects. Simulation software is used as a prediction tool to calculate the energy performance of buildings. However, numerous studies question its reliability due to the existing discrepancy (gap) between calculated and actual energy performance. Although occupant behaviour is identified as a factor of major impact on the energy performance of buildings, the complex stochastic nature of user behaviour makes it difficult to define actual occupancy patterns. As a result, standard and normative data are usually used as input in energy simulation models. The aim of this research is to test the effect of the use of actual presence profiles on energy demand simulations compared to the use of international normative presence profiles. A study on energy demand has therefore been developed, using dynamic simulation and monitoring campaigns. The results show that the heating and cooling energy demand may differ by up to 15% depending on whether actual or standard presence profiles are used. Therefore, presence profiles should be considered as a significant factor in the adjustment of input data in renovation projects. The final aim of this investigation is to determine the effect of using more accurate building and occupancy simulation parameters when assessing the feasibility of building renovation (payback period calculation for example). This paper focuses on the effect of presence profiles.

The Role of Occupants in Buildings’ Energy Performance Gap: Myth or Reality?

“…For single-family houses, the number of investigated buildings was equally distributed in the range between 1 and 10 as well as 11 and 100, with few studies above 100 [75,76]. The only other building-related information was dwelling size, reported by few studies [17,77]. It can be concluded that the resolution of available information on the investigated buildings tends to be low.…”

“…The characteristics reported are generally inconsistent across the studies due to differences in research foci and data availability issues. The most reported characteristics are the number of people [9,10,15,26,33,49,59,60,[79][80][81], age [9,10,15,17,21,26,33,59,77,80,81], household composition [6,7,12,14,17,30,59,77,80,82,83], and income [9,10,21,26,29,33,79,80]. Additional characteristics reported were ownership status [9,10,26,79,80,84] and education levels [17,21,…”

“…However, in most reviewed articles, the software applications are used to extract aggregated data to allow benchmarking against monitored data with similar granularities. Common metrics include absolute energy consumption (e.g., in kWh) [16], energy use intensity (e.g., kWh•m −2 •a −1 ) [17], or a similar CO 2 -focused metric (e.g., kg CO 2 •m −2 •a −1 ) [8]. Distinctions are often made between different fuel types and end-uses.…”

From Electricity and Water Consumption Data to Information on Office Occupancy: A Supervised and Unsupervised Data Mining Approach

“…Understanding the occupancy dynamics of the building, with other previously mentioned building variables creates knowledge that can be used for tasks such as energy, space and facility management, or even as an aid to the organisation's human resources department. For example, in the field of building simulation, occupant behaviour was recognised as a significant factor contributing to the high discrepancy between simulation prediction and real energy use [5][6][7][8]. Furthermore, Tianzhen et al [9] have listed ten challenges of building simulations where information on occupancy schedule is directly affecting three of them (addressing the building performance gap, modelling human-building interactions and energy model calibration).…”

“…Their work has tried to infer binary occupancy status (present, not present) and ranged occupancy (range of the number of present occupants). Since in the lab, the maximum occupancy is twelve persons, the authors have divided occupancy into four bins (0, 1-2, 3-5, [6][7][8][9][10][11][12]. Authors have used the following classifiers: NaiveBayes, Random Forest, Decision Tree, Multilayer Perceptron and KNN.…”