Abstract The use of Artificial Intelligence (AI) technologies in buildings can assist in reducing
energy consumption through enhanced control, automation, and reliability. This review aims …

Abstract Deep Neural Networks (DNN) are prominent Machine Learning (ML) algorithms
widely used, especially in medical tasks. Among them, Convolutional Neural Networks …

This paper is the editorial for the virtual special issue (VSI) of Renewable and Sustainable
Energy Reviews (RSER) dedicated to the 16th Conference on Sustainable Development of …

Although deep learning models have been rapidly developed, their practical applications
still lag behind for building energy systems (BESs) fault diagnosis. Owing to the “black-box” …

Accelerating the decarbonisation of the built environment necessitates increasing
electrification of end-uses, which in turn poses the issue of rethinking the role of energy …

Abstract The Energy Performance Gap (EPG) in buildings is a recognized phenomenon.
However, its definition and underlying factors are extensive, necessitating a thorough …

Machine learning (ML) algorithms are now part of everyday life, as many technological
devices use these algorithms. The spectrum of uses is wide, but it is evident that ML …

Accurate predictions of building energy consumption are essential for reducing the energy
performance gap. While data-driven energy quantification methods based on machine …

This research aims to diagnose the energy performance of buildings in València and identify
areas where energy efficiency can be improved. The energy performance results of all …

The urgency to combat climate change through decarbonization efforts is more crucial than
ever. The global building sector is one of the primary contributors to carbon emissions, yet …