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Afrooz Ebadat
Researcher at Royal Institute of Technology
Publications - 26
Citations - 343
Afrooz Ebadat is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: System identification & Model predictive control. The author has an hindex of 9, co-authored 26 publications receiving 298 citations. Previous affiliations of Afrooz Ebadat include Shiraz University.
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Proceedings ArticleDOI
Estimation of building occupancy levels through environmental signals deconvolution
TL;DR: This work addresses the problem of estimating the occupancy levels in rooms using the information available in standard HVAC systems with both online and offline estimators; the latter is shown to perform favorably compared to other data-based building occupancy estimators.
Journal ArticleDOI
Regularized Deconvolution-Based Approaches for Estimating Room Occupancies
TL;DR: The object of this study is the reconstruction of occupancy patterns in a room using measurements of concentration, temperature, fresh air inflow, and door opening/closing events, which are information sources often available in HVAC systems of modern buildings and homes.
Journal ArticleDOI
New fuzzy wavelet network for modeling and control:The modeling approach
TL;DR: A fuzzy wavelet network is proposed to approximate arbitrary nonlinear functions based on the theory of multiresolution analysis (MRA) of wavelet transform and fuzzy concepts.
Journal ArticleDOI
An application-oriented approach to dual control with excitation for closed-loop identification
TL;DR: Computationally tractable solutions based on Markov decision processes and model predictive control are presented for identification of systems operating in closed loop.
Proceedings ArticleDOI
Blind identification strategies for room occupancy estimation
Afrooz Ebadat,Giulio Bottegal,Damiano Varagnolo,Bo Wahlberg,Håkan Hjalmarsson,Karl Henrik Johansson +5 more
TL;DR: A two-tier estimation strategy for inferring occupancy levels from measurements of CO2 concentration and temperature levels, based on a frequentist Maximum Likelihood method or a Bayesian marginal likelihood method, implemented using a dedicated Expectation-Maximization algorithm.