Application of model predictive control for the optimization of thermo-hygrometric comfort and energy consumption of buildings

Application of model predictive control for the optimization of thermo-hygrometric comfort and energy consumption of buildings

Ludovico DanzaLorenzo Belussi Fabio Floreani Italo Meroni Andrea Piccinini Francesco Salamone 

Construction Technologies Institute of National Research Council of Italy, Via Lombardia 49, San Giuliano Milanese, Milano 20098, Italy

Consorzio Intellimech, Parco Scientifico Tecnologico Kilometro Rosso, via Stezzano, 87, Bergamo (BG), Italy

University of Bergamo, Department of Engineering and Applied Sciences, via Marconi, 5, 24044 Dalmine (BG), Italy

Corresponding Author Email:
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The use of tools of simulation in every field of engineering is in the last years widely spreading. Lot of them can be used and a large amount of simulators can be found on the market in order to perform every kind of analysis and prediction. In the field of building/plant system, tools based on white, grey and black box approaches are often used as a function of accuracy and reliability.

Several tools were developed according to mathematical models and transient analysis in order to perform Building Energy Simulations. The lumped capacitance models have a potential in terms of both data reliability and low computational cost.

The Resistance-Capacitance models can be realized with different orders to improve the dynamic thermal behavior of building and coupled with model-based design tools. Dymola with Modelica language can provide a useful tool for engineers to design a thermo-hygrometric comfort model optimizing the energy consumptions. The paper describes a calculation method developed with the aid of an outdoor test cell, based on a second order Lumped parameters model coupled with a hygrometric model and a Model Predictive Control thanks to a library for real time control and management of energy consumptions and thermal comfort.


building energy simulations, model predictive control, lumped parameters model, dymola

1. Introduction
2. Building model
3. Model predictive control
4. Predictive control results
5. Conclusions

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