Title: Green building development for a sustainable environment with artificial intelligence technology

Authors: Shanshan Wu

Addresses: Krirk University, Bang Khen District, Bangkok, Thailand; Hefei University of Economics, Anhui, Hefei, China

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%.

Keywords: green building; support vector machine; genetic programming; energy consumption; artificial intelligence; sustainable environment.

DOI: 10.1504/IJCAT.2023.135590

International Journal of Computer Applications in Technology, 2023 Vol.73 No.3, pp.203 - 216

Received: 06 Jan 2023
Accepted: 06 Mar 2023
Published online: 18 Dec 2023 *

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