The nearly zero-energy concept aims to achieve a significant reduction of energy
consumption in the buildings' sector, while promoting the renewable energy dissemination …

Buildings account for over one-third of global emissions and energy use. Meeting climate
pledges will require achieving high operational energy efficiency with low embodied impacts …

The energy performance of buildings was estimated using various data mining techniques,
including support vector regression (SVR), artificial neural network (ANN), classification and …

The first step in order to comply with the European Union goals of Near to Zero Energy
Buildings is to reduce the energy consumption in buildings. Most of the building …

To cope with 'Post-2020', each country set its national greenhouse gas (GHG) emissions
reduction target (eg, South Korea: 37%) below its business-as-usual level by 2030. Toward …

This paper proposes using evolutionary multivariate adaptive regression splines (EMARS),
an artificial intelligence (AI) model, to efficiently predict the energy performance of buildings …

Global energy concerns necessitate designing energy-efficient buildings. Many important
decisions affecting energy performance are made at early stages with little information …

Collecting data for machine learning (ML) development is a resource-intensive task that
necessitates identifying an efficient data collection approach. This study focuses on ML …

Buildings are one of the major consumers of global energy with a significant share reaching
to 40%. Phase change materials (PCMs) are used in building materials and structures for …

The article considers the effect of architectural volumetric design solutions on the demand
for energy and material resources for a building. The method used in the research aims at …