Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

Uncertainty analysis in building energy assessment has become an active research field
because a number of factors influencing energy use in buildings are inherently uncertain …

Accurate modelling of the weather's temporal and spatial impacts on building energy
demand is critical to decarbonizing energy systems. Here we introduce a customizable …

Statistical models can be used as surrogates of detailed simulation models. Their key
advantage is that they are evaluated at low computational cost which can remove …

In most of the countries, buildings are often one of the major energy consumers, leading to
the necessity of achieving sustainable building designs, and to the mandatory use of …

Increasing sustainability requirements make evaluating different design options for
identifying energy-efficient design ever more important. These requirements demand …

Buildings do not usually perform during operation as well as predicted during the design
stage. Disagreement between simulated and metered energy consumption represents a …

Nowadays, performing multi-objective optimizations of actual building designs is one of the
most challenging problems of the building energy efficiency area. This paper aims to …

Climate change exacerbates extreme heat events in the North Africa and Arabian Peninsula
(NAAP) region, posing a significant threat to human health and well-being. This study …

Surrogate models can emulate physics-based building energy simulation with a machine
learning model trained on simulation input and output data. The trained model is extremely …