P Pieter-Jan Hoes

International survey on current occupant modelling approaches in building performance simulation

It is not evident that practitioners have kept pace with latest research developments in building occupant behaviour modelling; nor are the attitudes of practitioners regarding occupant behaviour modelling well understood. In order to guide research and development efforts, researchers, policy-makers, and software developers require a better understanding of current practice and acceptance of occupant modelling. This paper provides results, analysis, and discussion of the results of a 36-question international survey on current occupant modelling practice and attitudes in building performance simulation. In total, 274 valid responses were collected from BPS users (practitioners, educators, and researchers) from 37 countries. The results indicate that most assumptions made about occupants vary widely and are considerably simpler than what has been observed in reality. Most participants cited lack of time or understanding as their primary reason for not delving deeply into occupant modelling, but responded that they are receptive to further training.

On the sensitivity to different aspects of occupant behaviour for selecting the appropriate modelling complexity in building performance predictions

The reliability of building performance simulation (BPS) predictions is impaired by a number of uncertainties, among which occupant behaviour (OB) plays a major role. Methods to relevantly model OB are essential to achieve energy efficient and comfortable buildings. This study contributes to the ongoing discussion concerning how to model OB in BPS. Specifically, a sensitivity analysis to various aspects of OB is used to assess the impact of using different levels of modelling complexity in the conceptual design phase. A method based on the statistical Mann–Whitney test is proposed to identify those aspects of OB that are influential for a performance indicator, and which might require a higher modelling complexity. Sixteen variants of an individual office constitute the case study. The results show how generalizations concerning robustness of a building typology to OB are not possible. Increasing modelling complexity does not necessarily lead to more accurate, or even to different results.

Integrating robustness indicators into multi-objective optimization to find robust optimal low-energy building designs

Uncertainties can have a large influence on building performance and cause deviations between predicted performance and performance during operation. It is therefore important to quantify this influence and identify robust designs that have potential to deliver the desired performance under uncertainties. Generally, robust building designs are identified by assessing the performance of multiple design configurations under various uncertainties. When exploring a large design space, this approach becomes computationally expensive and infeasible in practice. Therefore, we propose a simulation framework based on multi-objective optimization and sampling strategies to find robust optimal designs at low computational costs. The genetic algorithm parameters of optimization are fine tuned to further enhance the computational efficiency. Furthermore, a modified fitness function is implemented to use minimax regret robustness method in the optimization loop. The implemented simulation framework can save up to 94–99% of computational time compared to full factorial approach, while identifying the same robust designs.