Advanced forecasting and disturbance modelling for model predictive control of smart energy systems

published by ELSEVIER Applied Energy 292 (2021) 116889

co-authored by Christian Ankerstjerne Thilker, Henrik Madsen, John Bagterp Jørgensen from Technical University of Denmark, Department of Applied Mathematics and Computer Science, Asmussens Allé, Building 303B, DK-2800 Kgs. Lyngby, Denmark.

Keywords:
Model predictive control
Stochastic differential equations
Disturbance models
Smart energy systems

We describe a method for embedding advanced weather disturbance models in model predictive control (MPC) of energy consumption and climate management in buildings. The performance of certainty-equivalent controllers such as conventional MPC for smart energy systems depends critically on accurate disturbance forecasts. Commonly, meteorological forecasts are used to supply weather predictions. However, these are generally not well suited for short-term forecasts. We show that an advanced physical and statistical description of the disturbances can provide useful short-term disturbance forecasts. We investigate the case of controlling the indoor air temperature of a simulated building using stochastic differential equations (SDEs) and certaintyequivalent MPC using the novel short-term forecasting method. A Lamperti transformation of the data and the models is an important contribution in making this SDE-based approach work. Simulation-based studies suggest that significant improvements are available for the performance of certainty-equivalent MPC based on short-term forecasts generated by the advanced disturbance model: Electricity savings of 5%–10% while at the same time improving the indoor climate by reducing comfort violations by up to over 90%.

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