3D modeling of indoor spaces is a prerequisite for daylight simulation, and the accuracy of the 3D models has a significant impact on the simulation. The goal of this study was to quantify the errors caused by modeling indoor spaces at different accuracy levels to find the optimal balance between the reliability of the results and labor investment. For this purpose, we introduce a level of detail (LOD) concept for indoor spaces based on the size of non-permanent indoor objects by inclusion and exclusion from the simulation scene. The errors corresponding to models with low accuracies are measured by climate-based simulation using an improved two-phase method. Our results show that inaccurate modeling of indoor spaces causes between 10-70% error in TAI with 25% median across all spaces.@en

Optimizing the built environment via simulations of building models hinges on standardizing data acquisition. In this research, we put forward distinct levels of detail for geometry and material inputs, specifically tailored for indoor daylight applications. We primarily focus on understanding the uncertainties arising from imprecise estimations of material optical properties and incomplete geometrical inputs in climate-based indoor daylight simulations. Employing a Monte Carlo approach, we analyzed six office and teaching spaces, creating 20 variations for each by altering geometrical completeness and material accuracy. The technique of excluding non-permanent objects below certain sizes in four graduated steps was used to derive and test the impact of various geometrical levels of detail. Our findings reveal that different levels of geometrical completeness lead to errors ranging from 1.08% to 18.05%. Additionally, a twofold increase in simulation time was noted when geometrical detail was enhanced relative to the most basic model. Errors stemming from imprecise definitions of material optical properties showed a normal distribution. The uncertainty in simulation outcomes showed a linear rise with increasing input material uncertainty, lying between 10% to 30%, depending on space configurations. We observed heightened uncertainty near openings, attributed to window transmittance effects. The research underscores that daylight predictions are markedly more sensitive to transmittance uncertainties than to those in reflectance, regardless of the window-to-floor ratio. These insights may help to guide a more efficient data acquisition process of indoor spaces for daylight simulations.

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Recent studies have focused on data-driven methods for building energy efficiency, by using simulated or empirical data, for energy-based design assessment rather than the common physics-based techniques, which are mostly time-consuming. In this paper, the feasibility of using seven different Machine Learning models, including three single models and four ensemble ones, is studied to predict annual energy demand and thermal comfort of the model. For this purpose, 3024 synthetic samples of a single zone model with seven input features are simulated through the EnergyPlus engine for training in addition to 360 unseen samples as testing data for accuracy reporting. Heating and cooling demands, in addition to five annual thermal comfort indices, are calculated for each data point and used as target indices. Results show Extremely Randomized Trees and Random Forest models had the highest R2 of 0.99 and 0.85 for cooling and heating demands respectively. Also, the R2 of these models for predicting annual comfort was between 0.71 and 0.95. Results are then used to develop a prediction framework of thermal comfort and energy demand performance in the early stages of building design, where most of the information about building characteristics is not yet known.@en

A key aspect of daylight modeling is the definition of material optical properties. Characterization of such properties in existing indoor spaces with current methods is a labour-intensive and time-consuming task, especially in surfaces with considerable visual complexity. Faster and more accurate estimations of such properties will lead to more efficient workflows. Towards this direction, the present work studied the feasibility of using two novel approaches i.e. illuminance-proxy and probabilistic image based material characterization methods for implementation in daylight modeling. These approaches are compared with two common techniques, namely the manual selection from a measured dataset and the use of illuminance/luminance measurements. According to the results, both novel techniques are able to predict spatiallyaveraged Daylight Autonomy, continuous Daylight Autonomy, and Useful Daylight Illuminance in 300-3000 lx range with less than 5% error@en

The growing interest in reducing energy consumption and the associated environmental impacts are promoting energy efficiency in buildings. Comprehensive energy consumption models affected by occupant behavior are needed to assess the techno-economic implications of adopting energy conservation measures (ECM). In this study, occupant energy behavior (OEB) in university dormitories was determined based on field studies. The main contribution of this study is to identify the sensitivity of OEB scenarios (austerity, normal, energy spender) on energy savings via energy retrofit and present a decision-making tool to help prioritize ECMs based on payback and thermal comfort improvements. ECMs could improve energy efficiency by 32%, 56%, and 60% with the energy spender, normal, and austerity OEB models, respectively. Furthermore, the effectiveness of the ECMs does not follow a consistent pattern. The most effective actions in the normal OEB models are wall insulation and airtightness, and in the energy spender models, HVAC and lighting systems are the most effective ECMs. The integrated analysis of ECMs including energy savings, thermal comfort improvement, and paybacks is required considering different OEB scenarios because our study showed that an ECM might be a mid-cost in the case of energy spender, and a low-cost ECM in the case of austerity users.@en

Building energy simulation software are in no short supply. However, their excessive complexity makes many of them inaccessible to building engineers and architects, for early-stage design decisions. Within this context, many research groups and software developers have been working on tools that 1) are publicly-available, 2) require minimal input data, 3) have short run times, and 4) are suitable for benchmarking purposes and calculating energy or cost-savings. Recent trends show that many of these tools leverage web interfaces to visualize energy data for benchmarking, as well as simulation results for building energy analysis. The current review paper aims to provide detailed information and discussion of twenty-five web-based energy simulation tools, developed in the past few years. Different aspects of the tools, including calculation method, inputs, outputs, and capabilities, are investigated. The strengths and limitations of the tools and their future opportunities for developers and researchers are presented and discussed. Finally, a decision matrix is proposed to help users with the tool selection process, based on four main criteria, i.e. accessibility, capabilities, flexibility, and comprehensiveness.@en