Grey-box model for model predictive control of buildings

Abstract

Model predictive control (MPC) can improve energy efficiency and demand-side flexibility in buildings. Developing a grey-box model suitable for MPC is not straightforward, especially in buildings combining, not only ventilation and usual internal loads, but also Thermally Activated Building Structures (TABS) and large glass façades with external shading. To address these complexities, this paper presents a reduced order grey-box approach, considering all these elements. Various single zone model structures are compared, combining resistance–capacitance model, with finite difference or finite volume methods for modelling the TABS. The performance of these various model structures is evaluated using experimental data from a well-equipped living laboratory building. Additionally, the influence of technical parameters on the model's performance is 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.