Assessment of the Thermal Performance of Vertical Green Walls Using Overall Thermal Transfer Value Based BIM Simulation Method: Case Study of Residential Buildings in Sub-Tropics

Construction of multifunctional building envelopes using vertical greenery walls (VGW) has emerged as a sustainable green technology to improving cooling efficiency. To attaining the desired level of building cooling performance, VGW and overall thermal transfer value (OTTV) of the walls are useful design factors. The study aims to revise the current VGW evaluation, considering the decreased heat flux due to thermal efficiency of wall construction based on OTTV values. To achieve this, OTTV based Building Information Modelling (BIM) simulation method was proposed using Autodesk-Revit and DesignBuilder simulation based on EnergyPlus. Six wall compositions with various OTTV values of south facade for residential buildings located in sub-tropical in cooling season, were evaluated. The findings demonstrate that in the presence of a green system, a good OTTV value of the exterior walls is required for optimal performance, to keep the space within set point of cooling for long time during the cooling season. The comparisons between the bare walls and the VGW have demonstrated a great variation due to the different OTTV reached up to 6.57% and 18.44% reduction in indoor air temperature. The best combination of VGW resulted a maximum of 1.2°C reduction in indoor air temperature, with number of hours (within 28°C or less) were higher by 2506h, representing 85.59% of the overall number of hours (2928h). Overall cooling energy saving is found as 103.3kwh, representing 13.63% of the total of energy saving, and decreased the heat gained by 38.82%, representing 61.51kwh reduction during cooling season compared to base wall.

Comparative Investigation of Daylight Glare Probability (DGP) Comfort Classes in Clear Sky Condition

Glare is considered one of the most important variables to reach visual comfort and visual quality. It represents one of the fundamental barriers for an effective use of daylighting in buildings. One of the best performing and robust glare prediction models, relative to other available metrics, is a Daylight Glare Probability (DGP). Based on a validated and precise methodology (RADIANCE) the aim of this work is to compare the DGP model (original cut-off values) with new cut-off values that differ according to the time of day (morning, noon and afternoon). Both cut-off values were compared at more than 300 simulated conditions of daylighting in an interior space. This work offers the originality of studying recently proposed cut-off values in climate luminous with predominant clear sky conditions. Currently, the application of these new cutoff values is reduced to the field of science or simulation professionals. The results showed important differences (64.86%) between the categories proposed by both cut-off values. Nevertheless, these differences do not have a significant impact in glare prediction (< 2.7%), in terms of glare absence (DGP <0.38) and presence (DGP >0.38). This analysis made it possible: (i) to regionally apply the main current corpus criteria regarding glare issues as well as emergent proposals and (ii) to present new experimental data aimed at helping the field and, together with other works, improving the tools used by professionals on a daily basis.

Development of a Machine-Learning Framework for Overall Daylight and Visual Comfort Assessment in Early Design Stages

Application of machine learning methods as an alternative for building simulation software has been progressive in recent years. This research is mainly focused on the assessment of machine learning algorithms in prediction of daylight and visual comfort metrics in the early design stages and providing a framework for the required analyses. A dataset was primarily derived from 2880 simulations developed from Honeybee for Grasshopper. The simulations were conducted for a side-lit shoebox model. The alternatives emerged from different physical features, including room dimensions, interior surfaces’ reflectance factor, window dimensions, room orientations, number of windows, and shading states. Five metrics were applied for daylight evaluations, including useful daylight illuminance, spatial daylight autonomy, mean daylight autonomy, annual sunlit exposure, and spatial visual discomfort. Moreover, view quality was analyzed via a grasshopper-based algorithm, developed from the LEED v4 evaluation framework. The dataset was further analyzed with an artificial neural network algorithm. The proposed predictive model had an architecture with a single hidden layer consisting of 40 neurons. The predictive model learns through a trial and error method with the aid of loss functions of mean absolute error and mean square error. The model was further analyzed with a new set of data for the validation process. The accuracy of the predictions was estimated at 97% on average. The View range metric in the quality view assessment, mean daylight autonomy and useful daylight illuminance had the best prediction accuracy among others respectively. The developed model which is presented as a framework could be used in early design stage analyses without the requirement of time-consuming simulations.

Metamodeling of the Energy Consumption of Buildings with Daylight Harvesting – Application of Artificial Neural Networks Sensitive to Orientation

Daylight harvesting is a well-known strategy to address building energy efficiency. However, few simplified tools can evaluate its dual impact on lighting and air conditioning energy consumption. Artificial neural networks (ANNs) have been used as metamodels to predict energy consumption with high precision, few input parameters and instant response. However, this approach still lacks the potential to estimate consumption when there is daylight harvesting, at the ambient level, where the effect of orientation can be noted. This study investigates this potential, in order to evaluate the applicability of ANNs as a tool to aid the architectonic design. The ANNs were approached as metamodels trained based on EnergyPlus thermo-energetic simulations. The network configuration focused on determining its simplest feasible form. The input parameters adopted as the main variables of the building envelope were as follows: orientation, window-to-wall ratio and visible transmission. The effects of the encoding of orientation as a network input parameter, the number of examples of each variable for network training and changing the parameters used for the training were evaluated. The networks predicted the individualized consumption according to the end use with errors below 5%, indicating their potential to be applied as a simplified tool to support the design process, considering the elementary variables of the building envelope. The discussion of results focused on guidelines and challenges to achieve this purpose when contemplating the broadening of the metamodel scope.

Measurement, Simulation, and Quantification of Lighting-Space Flicker Risk Levels Using Low-Cost TCS34725 Colour Sensor and IEEE 1789-2015 Standard

Building owners are transitioning towards a smart lighting solution for illumination purposes. LED (Light Emitting Diode) lighting application has become a norm given its high efficacy and energy efficiencies. This paper presents an approach to monitor the percent flicker conformance of interior building lighting to international standards. The focus is on flickers induced by LED lightings. This experiment utilises a TCS34725 RGB (red, green, blue) colour sensor to measure the flicker parameters of interior lighting spaces. Light-sensitive photodiodes in the sensor detect changes in lighting intensity, and output digitised values. A Raspberry Pi4 minicomputer processes the data measured for comparison to several standards. Non-conformance is reported to building owners to take corrective actions and minimise flicker discomfort exposure to building occupants. A flicker risk level factor is determined to gauge the severity when flickers are present. This method may be used to replace luminaires or fix flickering lighting issues in buildings. The results show that the monitoring system is functional. The proposed measurement and data processing method can be incorporated into any smart building hub for automation and building performance analysis. The method may also be used to measure non-LED lighting flickers.

Improvement of Daylight Factor Model for Window Size Optimization and Energy Efficient Building Envelope Designs

Based on a new approach for the prediction of the Daylight Factor (DF), using existing empirical models, this research work presents an optimization of window size and daylight provided by the glazed apertures component for a building located in a hot and dry climate. The new approach aims to improve the DF model, considering new parameters for daylight prediction such as the orientation, sky conditions, daytime, and the geographic location of the building to fill in all the missing points that the standard DF, defined for an overcast sky, presents. The enhanced DF model is considered for the optimization of window size based on Non dominated Sorting Genetic Algorithm (NSGA II), for heating and cooling season, taking into account the impact of glazing type, space reflectance and artificial lighting installation. Results of heating and cooling demand are compared to a recommended building model for hot and dry climate with 10% Window to Wall Ratio (WWR) for single glazing. The optimal building model is then validated using a dynamic convective heat transfer simulation. As a result, a reduction of 48% in energy demand and 21.5% in CO2 emissions can be achieved. The present approach provides architects and engineers with a more accurate daylight prediction model considering the effect of several parameters simultaneously. The new proposed approach, via the improved DF model, gives an optimal solution for window design to minimize building energy demand while improving the indoor comfort parameters.

Optimum Characteristics of Windows in an Office Building in Isfahan for Save Energy and Preserve Visual Comfort

Nowadays, the use of renewable energies has increased due to the energy crisis and subsequent environmental issues. The window design significantly affects energy consumption and natural light absorption regarding preventing visual discomfort and improving indoor quality with effective external features. Hence, it should be carefully selected from the early stages of design. Thus, the present study investigated the optimal design of windows considering four components of the window-to-wall ratio (WWR), window shape, and positioning on each façade by separately considering the sill height of the window for a general office. The objective was to provide visual comfort and save energy. Applying constraints to the data set can yield an optimization method concerning the variables and their relationship as well as optimal solutions based on the stated goals. Therefore, the desired groups can be accepted as optimal solutions for improving the efficiency of the building. According to the results, the WWR of 30% with the square and horizontal shapes in the upper and central positions were optimal solutions for each window orientation, which had better performance in the north-facing WWR of 40%. Furthermore, several best design solutions were presented in each orientation in terms of energy consumption, daylighting, and visual comfort in the indoor environment. This method also allows the designer to visualize all the data while finding the clients’ desired option by improving the energy efficiency between the variables and choosing the appropriate solution.