AI-based KNN Approaches for Predicting Cooling Loads in Residential Buildings

Abstract

Cooling Load (CL) estimation in residential buildings is crucial for optimizing energy consumption and ensuring indoor comfort. This article presents an innovative approach that leverages Artificial Intelligence (AI) techniques, particularly K-Nearest Neighbors (KNN), in combination with advanced optimizers, including Dynamic Arithmetic Optimization (DAO) and Wild Geese Algorithm (WGA), to enhance the accuracy of CL predictions. The proposed method harnesses the power of KNN, a machine-learning algorithm renowned for its simplicity and efficiency in regression tasks. By training on historical CL data and relevant building parameters, the KNN model can make precise predictions, 768 sample with considering factors such as Glazing Area, Glazing Area Distribution, Surface Area, Orientation, Overall Height, Wall Area, Roof Area, and Relative Compactness. Two state-of-the-art optimizers, DAO and WGA, are introduced to refine the CL estimation process further. The integration of KNN with DAO and WGA yields a robust AI-driven framework proficient in the precise estimation of CL in residential constructions. This approach not only enhances energy efficiency by optimizing cooling system operations but also contributes to sustainable building design and reduced environmental impact. Through extensive experimentation and validation, this study demonstrates the effectiveness of the proposed method, showcasing its potential to revolutionize CL estimation in residential buildings. The results indicate that the hybridization of KNN with DAO optimizers yields promising outcomes in predicting CL. The high R2 value of 0.996 and low RMSE value of 0.698 demonstrate the accuracy of the KNDA model.