Iterative Platform for Predicting Housing Energy Performance in Lightweight Systems
DOI:
https://doi.org/10.46421/entac.v20i1.5783Keywords:
Machine learning, Decision tree, Computer simulations, Energy efficiency, Social housingAbstract
This study aimed to develop a design decision tool to assist designers in optimizing the energy performance of lightweight social housing envelopes. To achieve this objective, a decision tree method was employed to train a dataset consisting of 2048 simulations of a social housing utilizing a lightweight construction system for the climate of São Paulo, Brasil. By training the dataset using an algorithm provided by the Scikit-Learn library and implmented in the Python programming language, energy consumption could be predicted for various combinations of lightweight building system housing envelope. Subsequently,utilizing the benchmarking techiniques, a consumption scale was established to classify the dwelling into three performance levels: efficient; typical; or inefficient. Finally, an iterative interface was developed for analysing the energy performance of lightweight systems houses, enabling designers to identify combinations of construction parameters associated with lower energy consumption and higher levels of energy efficiency.
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