Samuel Prívara

TL;DR: In this article, the authors focus on the analysis of energy savings that can be achieved in a building heating system by applying model predictive control (MPC) and using weather predictions.

TL;DR: In this article, a model predictive controller (MPC) is applied to the temperature control of real building, which uses both weather forecast and thermal model of a building to inside temperature control.

TL;DR: In this paper, the authors provide an overview of identification methods for buildings and analyze their applicability for subsequent predictive control, and propose a new methodology to obtain a model suitable for the use in a predictive control framework combining the building energy performance simulation tools and statistical identification.

TL;DR: In this article, the optimization of the Predicted Mean Vote (PMV) index which, opposed to the static temperature range, describes user comfort directly has been studied and compared with a conventional and predictive control strategies.

TL;DR: In this paper, the authors present an approach in which a model of a building is selected by an iterative two-stage procedure, where a minimum set of disturbance inputs is formed so that the resulting model is the best with respect to a defined quality criterion.