The role of machine learning algorithms in enhancing energy efficiency and minimizing operational costs in smart infrastructure
DOI:
https://doi.org/10.29070/aaxfce54Keywords:
Machine Learning, Smart Infrastructure, Energy Efficiency, Operational Cost Reduction, Feature Selection, Neural Networks, Smart Buildings, Energy OptimizationAbstract
Construction is a significant consumer of energy and a significant contributor to carbon emissions; thus, it is imperative that intelligent solutions be implemented in order to enhance energy efficiency and reduce operating costs in the construction sector. Through the use of machine learning strategies, the objective of this study is to investigate the most effective ways in which smart infrastructure systems may maximize their energy efficiency. A quantitative strategy that was driven by data was used by our team. This method includes predictive modeling, feature engineering, and statistical correlation analysis, among other things. The temperature, lighting, heating, ventilation, and air conditioning (HVAC) systems, total occupancy, and interior equipment are the most significant aspects that influence energy use, as shown by Spearman's rank correlation. A number of machine learning models, including the XGB Regressor, the Keras Regressor, the Neural Network (MLP), the Random Forest Regressor, and the Support Vector Machine (SVR), were evaluated based on their performance using the Root Mean Square Error (RMSE). It was via the use of feature selection and feature importance procedures that the performance of the model was considerably enhanced. Based on the findings, it was determined that the optimization of Neural Network (MLP) algorithms with Variance Threshold and LASSO algorithms yielded the highest level of prediction accuracy, accompanied by the lowest RMSE values. On the other hand, statistical feature extraction brought to an improvement in generalization in ensemble models such as XGBoost and Random Forest. The findings indicate that the use of appropriate feature engineering approaches has a significant impact on the outcomes of operational efficiency and prediction accuracy. This research highlights the real-world potential of energy management systems that are driven by machine learning to reduce operational expenditures and energy use in situations that are associated with smart infrastructure.
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