Designers need to compare numerous design options in the process of designing an energy-efficient building. There are two impediments in this process, first probabilistic prediction of energy requirement at an early stage of design with uncertain design parameters and, second, selecting a design option based on the probabilistic energy prediction. The paper presents an integration of machine learning energy prediction model with building information modelling (BIM) tool to make probabilistic energy prediction, i.e. ranges of values. Wilcoxon rank-sum test is useful in this situation, which is capable of comparing alternatives based on probabilistic energy predictions. The tool has been developed to extract information from the BIM model, make probabilistic energy prediction using the Monte Carlo method, and perform statistical analysis. It has been found that BIM integrated machine learning model can make energy prediction of six design alternatives in 30-35 seconds with no additional modelling efforts. Higher uncertainty in the design parameters will result in larger uncertainty in the energy prediction, and the test may not be able to suggest the better option even using statistical comparison. This will require the more precise value of design parameters, i.e. reduced uncertainty. Different uncertainty levels in the design parameters have been tested to which extent they are sufficient to make a selection of the energyefficient option. It is observed that uncertainty levels that are suitable for decision-making depend on the combination of design options to be compared. It is possible to differentiate among alternatives with high uncertainty in the design parameters if they are entirely different else more precise definition of the design parameters is required. This research provides a method to select a better option among the developed options based on energy performance at the early stage of design.
It has been a challenging endeavour for designers to develop an energy-efficient design to meet
the demand for low energy use buildings. The first step in this endeavour to design a building
envelope which results in lower energy loads, i.e. heating and cooling load
There are two primary challenges for informed decision-making at the early stage for
energyefficient design. First, make probabilistic energy performance prediction of the design options
with uncertain design parameters and, second, use the probabilistic prediction to identify better
solutions. Since, a number of design parameters influencing the energy loads are uncertain at
the early stage of design
The energy performance of a design option is predicted using this approach, as probabilistic
heating and cooling loads. The probabilistic heating and cooling loads will be a range of values;
thus, it is not possible to compare the design options using quantitative comparison. The paper
proposes the use of statistical method, Wilcoxon rank sum test, to assess whether two design
options are significantly different from each other
The energy performance analysis tools offer limited integration with the design process due to
the need for extensive modelling efforts and high computational time
One challenge of energy prediction at an early stage of design is uncertain design parameters.
This issue makes the energy performance prediction process more complicated. Gelder et al.,
(2014) has proposed the use of probabilistic prediction of energy performance with uncertain
information using the Monte Carlo method and engineering surrogate models. The probabilistic
estimation of energy performance with uncertain inputs is possible with the use of time-efficient
meta-models and machine learning models
The decision-making exercise for building design is very complex, which involve assessment
of building based on several contradicting performance measures such as energy consumption
and thermal comfort etc. The simulation tools are used in assessing the building on these
performance measures and assist in selecting a better option
Wilcoxon rank sum test is used to test whether the randomly selected value from one sample is
statistically different from a randomly selected value from another sample
The research methodology is described in two parts: First, integrated tool for quick energy prediction and, second, statistical analysis to differentiate among options.
CBML model is developed based on the decomposition of an energy prediction model to a
building element level
2Window construction parameters – the window to wall ratios in each direction, u-value and gvalue of windows
In this research, newly developed CBML is used. It follows a similar component structure as in Geyer and Singaravel, 2018. The training data is enriched with more architectural design cases to get better performance of ML models. The newly developed models are tested on the design case outside of training data, and the performance of the models is described in Figure 2. The model shows the goodness-of-fit (R2) 0.998 and 0.986 for heating and cooling load, respectively. Thus, the model can be used for energy load predictions. The authors have developed a method integrated with a BIM authoring application to automate the process of making probabilistic energy predictions using CBML. The complete process of integration is described in Figure 3. The geometrical parameters are extracted from the BIM model, which is available as design information in the model. The remaining parameters are unknown at an early stage of the design and collected using a user input form as uncertain information. This information is passed to the CBML model to make energy predictions and integrate the results back to the BIM model.
The results are statistically analysed using non-parametric test - Wilcoxon rank sum test.
Wilcoxon rank sum test is used to determine whether two independent samples selected from
the population are significantly different from each other
The method is implemented a testcase building Tausenpfund – an average size office building in Munich. It has a rectangular floor plan with a floor area of 1200 sq. m. (3×14.8×27), distributed equally on three floors. The method is implemented on five design options developed as alternatives to rectangular floor plan building.
The method based on the CBML model is integrated with BIM application Autodesk Revit to demonstrate the presented approach. The six design options are stored as conceptual masses in the application and accessed through the plugin. The successful completion of the tool allows the energy prediction and integration of energy prediction results back to the BIM model. The accuracy of the prediction is the same as the accuracy of CBML. In terms of timeefficiency, it takes 30-35 seconds to make energy prediction of six design options using 500 random samples. It means a total of 3000 energy models are evaluated in approximately 30 seconds. The process may take up to 24 hours using traditional energy simulation tools. The energy prediction is made based on the mass model and user input, which requires no additional modelling for energy prediction.
Four scenarios with different level of uncertainty for input parameters are analysed to ascertain whether it is possible to make a distinction between the design options. There is a total of 15 combinations for six design options. The level of uncertainty and corresponding significant pvalues for heating and cooling load is presented in Table 2 and Figure 5. The number of significant p-values is higher as the level of uncertainty decreases. With the uncertainty of ±25% in technical specification and operational design parameters and ±50% in window construction parameters, the number of significant p-values is 6 and 8 for heating and cooling load respectively for a total of 15 combinations. The number of significant p-values is 15 and 14 for heating and cooling load, respectively when the uncertainty in all the uncertain parameters is ±5%. This paper has presented an approach of BIM integrated CBML model for probabilistic energy prediction at an early stage of design in a time-efficient way. The subsequent application of statistical analysis indicated for decision-making if two options show a significant difference in their probabilistic energy prediction of designed alternatives. The presented approach of making probabilistic estimation and statistical analysis has several limitations which are discussed in this section. The presented approach utilises CBML model, which has a limitation as all ML models, which is the applicability of models to new design cases, i.e. the generalisation. In the presented case, CBML model covers the range of design cases mentioned in the approach and energy prediction results are trustworthy. Hence, the applicability of CBML model needs to be tested extensively before using it on new design case beyond the range. A comparison of the final design configuration is recommended in this situation. The method is only applicable to uniform floor plan buildings. The generation of building elements based on the one-zone-per-floor rule is another simplification of the energy model. On the one hand, adaptions of the method for floor plans that differ on each level are possible; however, this requires a trade-off between modelling efforts and model accuracy as well as training effort. On the other hand, such simplifications are commonly used for energy prediction at the early stage and comply with the approach of architectural design. The essential advantage of the CBML method is that it allows a higher degree of flexibility than monolithic parametric models and provides reusability of the component model and easy integration with BIM model. Flexible detail levels and creation of missing building elements are possible by generative methods.
The approach presents four scenarios with varying levels of uncertainty in the design parameters. It can be noted that the number of significant p-values increases with a lesser level of uncertainty. This fact shows the possibility of differentiating among all the options with a decreasing level of uncertainty. Also, the p-values dependent on the combination of options to be compared, so some combinations are different from each other even with a higher level of uncertainties and some are not different from each other even with precise value of design parameters. Thus, it may be possible that a designer needs to detail out one or more options before the uncertain parameters are defined more precisely.
The level of uncertainty in the design parameters is assumed to be uniformly distributed between the range of mean ± variation, however other distributions such as triangular or normal distributions are also possible which may result in lesser uncertainty in the energy prediction results. Which means the decision can be made with higher uncertainty in the design parameters. Also, if the energy prediction results are normally distributed because of another type of distribution in the design parameters, it will be possible to use a powerful parametric statistical test. 6
The architectural design is assisted by making a probabilistic prediction of energy performance and statistically analysing the results for decision making. The approach offers design assistance in requiring information and prioritising decisions at the early stage of design by making quick energy prediction with BIM integrated machine learning-based energy prediction model and analysing the response statistically.
The process of making energy prediction is streamlined with the use of BIM integrated machine learning model at building element level. The inherent uncertainty at the early stage of the design reflects in the energy prediction process. It requires an evaluation of several energy models for making probabilistic energy predictions. The whole process is made faster by extraction of data from the BIM model and energy predictions using CBML method. This method provides quick energy prediction results for the comparison of alternatives. In contrast to tradition energy simulation tool, the machine learning model is at least 3000 times faster. The accuracy of energy prediction is the same as the accuracy of the developed CBML mode, which is 0.998 and 0.986 (R2 values) for heating and cooling load, respectively. The uncertainty in the design parameters gets translated into the uncertainty in the energy predictions, which requires statistical analysis for comparison. The statistical test allows comparing the architectural design option considering the uncertainty that is present at the early stage of design. The possibility to compare the design alternatives depends on the uncertainty in the design parameters and the combination of design alternatives, as shown in the case presented in this paper. It will require a more precise definition of the design parameters to differentiate between two similar design options if the statistical comparison is not possible. The integrated tool with BIM and CBML model offers an opportunity to address the design of energy-efficient buildings. There are some limitations of the current implementation of the approach such as simplifications of the energy model, applicability of machine learning model to new design case, uniform distribution of the design parameters and data extraction possible only for uniform floor plan buildings. It will require future research to address these limitations and to extend the applicability of the approach to more design case.
The research was funded by the Deutsche Forschungsgemeinschaft (DFG) in Researcher Unit 2363 “Evaluation of building design variants in early phases using adaptive levels of development”, in Subproject 4 “System-based Simulation of Energy Flows”. The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government – department EWI.