Grey-box model for model predictive control of buildings
Peter Klanatsky, François Veynandt, Christian Heschl
Abstract
Model predictive control (MPC) can improve energy efficiency and demand-side flexibility in buildings. Developing a suitable grey-box model is not straightforward, especially in buildings combining, beside ventilation and usual internal loads, Thermally Activated Building Structures (TABS) and large glass façades with external shading. Therefore, this paper presents a reduced order grey-box approach, considering all these elements. Several single zone model structures are compared, combining resistance–capacitance model, with finite difference or finite volume methods for the TABS. The performance of these various model structures is evaluated using experimental data from a well-equipped living laboratory building. The influence of technical parameters is also investigated. The best model variant, with an enhanced glass façade model, achieves an accuracy of 0.25 °C of Mean Absolute Error over a year of simulation, on the 24 h zone temperature forecast compared to the measurement. This model has a small number of parameters (8), which are estimated with the least square non-linear method. The stability of the parameter values is analysed. The parameter identification requires only a small historical dataset of 1–2 weeks for startup and 2–4 weeks for training. This provides an adaptive model, in the sense that it is updated regularly (every day or week) based on recent measurement data. This data-driven evolving model is suitable across a wide range of applications involving data-driven Model Predictive Control (MPC) for buildings.