Energy efficient building climate control using Stochastic Model Predictive Control and weather predictions

By Frauke Oldewurtel, Alessandra Parisio, Colin Neil Jones, Manfred Morari, D. Gyalistras, M. Gwerder, V. Stauch, B. Lehmann, K. Wirth. Proceedings of the 2010 American Control Conference, 2010

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Review on model predictive control: an engineering perspective (2021, background): “Often, individual rooms were modeled as capacity resistor elements [82, 90, 91, 107].”
Stochastic Model Predictive Control: An Overview and Perspectives for Future Research (2016, unknown): “To enable dealing with unbounded disturbances, the hard input bounds considered in [47] are relaxed in [48] so that the inputs are restricted to lie in a subset with a prescribed probability level.”
Demand Response-Enabled Model Predictive HVAC Load Control in Buildings using Real-Time Electricity Pricing (2013, methodology): “They investigated a bilinear model under stochastic uncertainty with probabilistic, time varying constraints (Oldewurtel et al., 2010a).”
A method for model-reduction of non-linear thermal dynamics of multi-zone buildings (2012, background): “To improve energy fficiency, there is a growing interest in developing techniques that ompute control signals that minimize building-wide energy conumption [1–5].”
Sentinel: occupancy based HVAC actuation using existing WiFi infrastructure within commercial buildings (2013, background): “Literature has shown that predictive control with deep setpoints can lead to higher energy savings in HVAC systems [13, 28, 32, 40].”
Deep reinforcement learning for building HVAC control (2017, background): “In the literature, many approaches have been proposed to control building HVAC systems for energy efficiency [15, 11, 10, 25].”