The computerized simulations of physical and socio-economic systems have proliferated in
the past decade, at the same time, the capability to develop high-fidelity system predictive …

This article reviews the applications of artificial neural networks (ANNs) in greenhouse
technology, and also presents how this type of model can be developed in the coming years …

This study proposes a novel framework and methodology for building digital twinning (BDT)
over the life cycle of a building. It aims to establish an intrinsic digital twin framework and …

The building information and operational data generated during the building life cycle are
essential for realizing energy-efficient operation, indoor environmental quality, and …

This paper presents a heuristic model predictive control (MPC) methodology to activate
energy flexibility in fully electric school buildings in cold climates to reduce electricity …

Abstract The European Council has proposed reducing buildings' energy consumption as
one way to decarbonization by 2050. Currently, digital twins are used for real-time energy …

Phase change materials (PCMs) are recognized as a promising passive energy retrofit
technology for existing buildings, as they are applied to the internal and external surfaces of …

The digital world is spreading to all sectors of the economy, and Industry 4.0, with the digital
twin, is a reality in the building sector. Energy reduction and decarbonization in buildings are …

This paper presents a methodology for the development and implementation of Model
Predictive Control (MPC) in institutional buildings. This methodology relies on Artificial …

Decarbonisation and efficiency goals set as a response to global warming issue require
appropriate decision-making strategies to promote an effective and timely change in energy …