A Markov Process Approach to Ensemble Control of Smart Buildings
For smart building control researchers, this offers a simplified modeling approach that integrates real-time data, though the improvement is incremental.
The paper proposes a Markov Process approach to model building energy consumption, reducing complexity and parameter dependency while maintaining accuracy for system-level analyses. The method is validated using building data from Belgium.
This paper describes a step-by-step procedure that converts a physical model of a building into a Markov Process that characterizes energy consumption of this and other similar buildings. Relative to existing thermo-physics-based building models, the proposed procedure reduces model complexity and depends on fewer parameters, while also maintaining accuracy and feasibility sufficient for system-level analyses. Furthermore, the proposed Markov Process approach makes it possible to leverage real-time data streams available from intelligent data acquisition systems, which are readily available in smart buildings, and merge it with physics-based and statistical models. Construction of the Markov Process naturally leads to a Markov Decision Process formulation, which describes optimal probabilistic control of a collection of similar buildings. The approach is illustrated using validated building data from Belgium.