This study aims to enhance energy performance estimation and maintenance planning for office buildings in Tehran. Machine learning and data mining techniques were employed, utilizing Support Vector Machines (SVMs), Ar...
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This study aims to enhance energy performance estimation and maintenance planning for office buildings in Tehran. Machine learning and data mining techniques were employed, utilizing Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Narrow Neural Networks, and optimizable SVMs. A comprehensive dataset of 2500 experimental samples was utilized with five input parameters (building size, occupancy patterns, environmental conditions, and ACB system parameters) and one output parameter (electricity consumption). gpr models performed better in estimating electricity consumption, achieving a Root Mean Square Error (RMSE) of 297.01 and an impressive R-squared value of 0.99. To optimize energy performance and design more energy- efficient buildings, this model accurately predicts energy consumption. Additionally, this study addressed maintenance planning for ACB systems, simulating maintenance issues with 2500 synthetic samples. The dataset included building age, size, number of floors, usage intensity, previous maintenance history, location, and environmental factors. Maintenance time was estimated using these features, and XGBoost was employed to predict ACB system maintenance time. Low MSE, RMSE, and R-squared values in the XGBoost model allow for accurate predictions. Using this model, facility managers can proactively plan maintenance activities, allocate resources, and minimize downtime. Through proactive measures, ACB performance and maintenance costs are optimized. The study accurately predicts ACB system energy consumption and maintenance time using the rational quadratic gpr model, applicable in Tehran and beyond. Additionally, MATLAB version R2022b was used for prediction, while DesignBuilder and EnergyPlus were utilized for technical analysis.
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