Green building development for a sustainable environment with artificial intelligence technology Online publication date: Mon, 18-Dec-2023
by Shanshan Wu
International Journal of Computer Applications in Technology (IJCAT), Vol. 73, No. 3, 2023
Abstract: A considerable quantity of CO2 has been delivered into the air due to construction, building operations, and bad energy sources. This study aimed to assess the effectiveness of using AI in Green Building Construction (AI-GBC) to decrease greenhouse gases and energy consumption. Support Vector Machine (SVM) and Genetic Algorithm (GA) are employed in AI to reduce CO2 emission and energy consumption. Multiple statistical metrics, such as the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Root Mean Squared Log Error (RMSLE) are employed to evaluate the accuracy of the AI-GBC. Results from Machine Learning (ML) models were satisfactory, with both achieving high levels of prediction accuracy as 95%. With the R2 value of 0.95 for CO2 prediction, GA models accurately predicted values congruent with experimental data. Performance analysis will be achieved by 96%, and analysis of k folds cross-validation will be achieved by 97%.
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