Abdul Afram

TL;DR: In this paper, the authors present a literature review of model predictive control (MPC) for HVAC systems, with an emphasis on the theory and applications of MPC for heating, ventilation and air conditioning (HVAC) systems.

TL;DR: In this paper, a comprehensive review of the artificial neural network (ANN) based model predictive control (MPC) system design is carried out followed by a case study in which ANN models of a residential house located in Ontario, Canada are developed and calibrated with the data measured from site.

TL;DR: A review of the methods used to model the heating, ventilation, and air conditioning (HVAC) systems can be found in this article, where major data driven, physics based, and grey box modeling techniques reported in the recent literature are reviewed.

TL;DR: In this article, gray-box models of the residential heating, ventilation and air conditioning (HVAC) system were developed for the TRCA Archetype Sustainable House (TRCA-ASH) HVAC systems located at Kortright Centre for Conservation in Vaughan, Ontario, Canada.

TL;DR: In this article, black-box models of the residential heating, ventilation and air conditioning (HVAC) system are developed using the system identification techniques in Matlab® and the developed models include models based on multiple-input and multiple-output (MIMO) artificial neural network (ANN), transfer function (TF), process, state-space (SS) and autoregressive exogenous (ARX) ones of each HVAC subsystem.