Farrokh Janabi-Sharifi

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: A first-order adaptive windowing method is shown to be optimal in the sense that it minimizes the velocity error variance while maximizes the accuracy of the estimates, requiring no tradeoff.

TL;DR: A new algorithm, namely iterative adaptive EKF (IAEKF), is proposed by integrating mechanisms for noise adaptation and iterative-measurement linearization and the experimental results are provided to demonstrate the superiority of IAEKF in dealing with erroneous a priori statistics, poor pose initialization, variations in the sampling rate, and trajectory dynamics.