Energy Efficiency in Smart Buildings through Prediction modeling and Optimization Using a Modified Whale Optimization Algorithm

Abstract

This paper presents a comparative study on the prediction of energy consumption in buildings using machine learning techniques. The dataset encompasses a diverse range of buildings with 8 input features and one output variable, representing the energy consumption. The primary focus is on evaluating the performance of two prominent and widely-used machine learning algorithms: Artificial Neural Networks (ANN) and Random Forest (RF). The results indicate a promising predictive capacity of both models, showcasing their effectiveness in capturing intricate patterns within the dataset. In the case of ANN, the Root Mean Squared Error (RMSE) is reported at 3.806, demonstrating the model's ability to approximate the true energy consumption values. Furthermore, the Random Forest model exhibits enhanced predictive accuracy, as reflected by a lower RMSE of 1.392. In addition to predictive analysis, this study utilizes a Modified Whale Optimization Algorithm (MWOA) to optimize energy consumption. The MWOA helps to identify the associated input values that lead to the lowest possible energy consumption, providing valuable insights for energy-efficient building design. The implications of this research extend to the broader field of sustainable architecture and urban planning, paving the way for more informed decisions aimed at reducing energy consumption and fostering environmental sustainability.