Model Predictive Control Based on Stochastic Grey-Box Models

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The future energy system is weather-driven. To take full and effective advantage of the renewable energy production, we need to make the demand flexible, such that it better coincides with the weather-driven energy production. We argue that this disruption of the energy system implies a need for new planning and control methodologies for the energy demand. This chapter briefly introduces the grey-box modelling principles (which facilitate the need for real-time applications) and illustrates how to use the grey-box models for multi-level control systems of smart buildings and neighbourhoods. Utilising the inherent energy flexibility of buildings, we describe how to use a dynamic price signal to make buildings behave in a desired way for better grid and energy balancing. Each building individually plans its own energy consumption based on the price signal, but it also needs to consider the local weather. We introduce a novel stochastic dynamical model for an integrated setup of grey-box models that are able to take advantage of the local weather disturbances. Finally, we show how to control a building given weather-driven disturbances using model predictive control and integrated grey-box model formulations.
Original languageEnglish
Title of host publicationTowards Energy Smart Homes
Publication date2021
ISBN (Print)978-3-030-76476-0
Publication statusPublished - 2021
SeriesTowards Energy Smart Homes


  • Grey-box models
  • Model identification
  • Parameter estimation
  • Model predictive control
  • Stochastic differential equations
  • Disturbance models
  • Smart energy systems


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