Previous studies have identified significant demand response (DR) potentials in using
economic model predictive control (E-MPC) of space heating to exploit the inherent thermal …

Several studies have indicated a significant potential in using Model Predictive Control
(MPC) of space heating for demand response purposes. The performance of the MPC …

Grey-box models are data-driven models where the structure is defined by the physics while
the parameters are calibrated using data. Low-order grey-box models of the building …

A grey-box model is a combination of data-driven and physics-based approaches to
modeling. For applications in buildings, grey-box models can be used as the control model …

An energy model for non-residential buildings based on state space equations has been
developed and experimentally validated. This model can predict with precision the Heating …

Modeling and control of the heating feature of living spaces remain challenging tasks
because of the intrinsic nonlinear nature of the involved processes as well as the strong …

Data-driven applications in buildings using AI and machine learning have generated a lot of
interest, but scaling these applications is challenging due to the uniqueness of each …

The capability to dynamically plan, predict, and control indoor conditioning allows to adapt to
individual preferences of inhabitants and enables demand side management. While former …

In the course of the research, the authors diagnosed the necessity to constantly search for
solutions increasing the efficiency of converting primary energy into useful energy, in …

In today's world, where technology is rapidly evolving and people are becoming more
concerned about cost and comfort, the demand for electricity for heating, ventilation, and air …