Identification of the Factors Affecting the Reduction of Energy Consumption and Cost in Buildings Using Data Mining Techniques
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Optimizing energy consumption and coordination of utility systems have long been a concern of the building industry. Buildings are one of the largest energy consumers in the world, making their energy efficiency crucial for preventing waste and reducing costs. Additionally, buildings generate substantial amounts of raw data, which can be used to understand energy consumption patterns and assist in developing optimization strategies. Using a real-world dataset, this research aims to identify the factors that influence building cost reduction and energy consumption. To achieve this, we utilize three regression models (Lasso Regression, Decision Tree, and Random Forest) to predict primary fuel usage, electrical energy consumption, and cost savings in buildings. An analysis of the factors influencing energy consumption and cost reduction is conducted, and the decision tree algorithm is optimized using metaheuristics. By employing metaheuristic techniques, we fine-tune the decision tree algorithm's parameters and improve its accuracy. Finally, we review the most practical features of potential and nonpotential buildings that can reduce primary fuel usage, electrical energy consumption, and costs
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