Artificial Neural Networks for Predicting Energy Performance and Consumption in Buildings: A Comprehensive Review

Abstract

As the world's population, urban infrastructure, and technological capabilities continue to expand rapidly, so does the demand for energy. This paper aims to emphasize the importance of prioritizing energy efficiency in new buildings and enhancing the energy performance of existing structures. This study reviews various machine learning (ML) models and their applications in building energy forecasting, comparing and contrasting their effectiveness. In recent years, ML approaches, particularly Artificial Neural Networks (ANNs), have been proposed for predicting energy consumption and performance in buildings. This paper discusses these models in the context of building energy forecasting. Furthermore, it explores the application of digital twins beyond the construction industry, highlighting their potential benefits in asset lifecycle management and optimization. By providing a comprehensive reviews of ML models and exploring the potential of digital twins, this research contributes to developing effective strategies for reducing energy consumption in the building sector.