Search

Thermochromic (TC) materials can switch solar absorptance (α) based on temperature stimuli. When coatings with TC properties are applied on building envelope surfaces, the amount of solar heat gains can be controlled to reduce the heating and cooling demand of buildings. To date, limited research has been conducted in investigating optimal TC coating properties for application on opaque building envelopes in various scenarios. In this research, a method to model TC coatings using building performance simulation (BPS) tools has been developed and coupled with python to optimize solar absorption states (α) and switching temperatures and reduce the annual heating and cooling demand. The simulation-based approach has been employed to perform early-stage exploration studies on multiple building types and climates to support material R&D in developing optimized coatings for target applications and assess the potential energy savings. The results indicate that the optimum TC properties are unique to climate and building types. TC coatings with high switching temperatures result in larger energy savings for scenarios with high heating demands, while TC coatings with low switching temperatures produce larger energy savings in scenarios with high cooling demands. Similarly, increasing the high solar absorption (αhigh) to 1 increases the heating savings, while reducing the low solar absorption (αlow) to 0 results in higher cooling savings. Furthermore, it was found that solar irradiance causes temperature spikes triggering the TC coatings to unnecessarily switch from high to low absorptance state in winters leading to heating penalties. Replacing optimal static with TC coatings on terraced houses in the Spanish climate with a 2:3 heating to cooling demand ratio results in 2 to 13% energy savings.

This paper presents a structured, generically applicable, method for using building performance simulation to aid the development of comfort-driven solar shading controls by mapping predicted occupant comfort conditions to sensor measurements. The method uses confusion matrices as a statistical classification approach to facilitate (i) selection of sensor deployment strategies that offer beneficial trade-offs considering multiple performance aspects and (ii) identification of control algorithms that optimise comfort conditions using non-ideal sensors. The support method requires relatively little effort from a developer, only a small number of simulations and fits well within the current practice of shading control development. The method is tested using a sun-tracking control strategy for indoor roller blinds as a case study, which demonstrates that the method can identify high-performance solutions. Finally, generally applicable features of the method are extrapolated from the case study, and alternative applications and the method’s limitations are discussed.

Building Energy Simulation (BES) tools play a key role in the optimization of the building system during the different phases, from pre-design through commissioning to operation. BES tools are increasingly used in research as well as in companies. New BES tools and updated versions are continuously being released. Each tool follows an independent validation process but rarely all the tools are compared against each other using a common case study. In this work, the modelling approaches of widespread dynamic simulation tools (i.e. EnergyPlus, TRNSYS, Simulink libraries CarnotUIBK and ALMABuild, IDA ICE, Modelica/Dymola and DALEC), as well as PHPP (a well-known quasi-steady-state tool), are described and the results of all the tools modelling the same characteristic office cell, defined within the IEA SHC Task 56, are compared on a monthly and hourly basis for the climates of Stockholm, Stuttgart and Rome. Unfortunately, different tools require different levels of input detail, which are often not matching with available data, hence the parametrization process highly influences the quality of the simulation results. In the current study to evaluate the deviation between the tools, frequently used statistical indices and normalization methods are analysed and the problems related to their application, in a cross-comparison of different tools, are investigated. In this regard, the deviation thresholds indicated by ASHRAE Guideline 14-2014 are used as a basis to identify results that suggest an acceptable level of disagreement between the predictions of a particular model and the outcomes of all models. The process of reaching a good agreement between all tools required several iterations and great effort on behalf of the modellers. To aid the definition of building component descriptions and future references for inter-model comparison a short history of the executed steps is presented in this work. Together with the comparison of the results of the tools, their computa