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:
30 September 2018
| Citation



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

Amara F., Agbossou K., Cardenas A., Dubé Y., Kelouwani S. (2015). Comparison and simulation of building thermal models for effective energy management. Smart Grid and Renewable Energy, Vol. 6, No. 4, pp. 95.

Andersen K. K., Madsen H., Hansen L. H. (2000). Modelling the heat dynamics of a building using stochastic differential equations. Energy and Buildings, Vol. 31, No. 1, pp. 13-24.

Bacher P., Madsen H. (2011). Identifying suitable models for the heat dynamics of buildings. Energy and Buildings, Vol. 43, No. 7, pp. 1511-1522.

Belussi L., Danza L., Meroni I., Salamone F. (2015). Energy performance assessment with empirical methods: Application of energy signature. Opto-Electronics Review, Vol. 23, No. 1, pp. 85-89.

Belussi L., Danza L., Meroni I., Salamone F., Ragazzi F., Mililli M. (2013). Energy performance of buildings: A study of the differences between assessment methods. In Energy Consumption: Impacts of Human Activity, Current and Future Challenges, Environmental and Socio-Economic Effects; Nova Science Publishers: New York, NY, USA, pp. 53-75.

Berthou T., Stabat P., Salvazet R., Marchio D. (2014). Development and validation of a gray box model to predict thermal behavior of occupied office buildings. Energy and Buildings, Vol. 74, pp. 91-100.

Bünning F., Sangi R., Müller D. (2017). A Modelica library for the agent-based control of building energy systems. Applied Energy, Vol. 193, pp. 52-59.

Coakley D., Raftery, P., Keane M. (2014). A review of methods to match building energy simulation models to measured data. Renewable and Sustainable Energy Reviews, Vol. 37, pp. 123-141.

Communication from the Commission. (2014). A policy framework for climate and energy in the period from 2020 to 2030. Brussels, Belgium.

Danza L., Barozzi B., Belussi L., Meroni I., Salamone F. (2016). Assessment of the performance of a ventilated window coupled with a heat recovery unit through the co-heating test. Buildings, Vol. 6, No. 1, pp. 3.

Danza L., Belussi L., Meroni I., Mililli M., Salamone F. (2016). Hourly calculation method of air source heat pump behavior. Buildings, Vol. 6, No. 2, pp. 16.

Danza L., Belussi L., Meroni I., Salamone F., Floreani F., Piccinini A., Dabusti A. (2016). A simplified thermal model to control the energy fluxes and to improve the performance of buildings. Energy Procedia, Vol. 101, pp. 97-104.

Danza L., Belussi L., Guazzi G., Meroni I., Salamone F. (2018). Durability of technologies in the keeping of ZEB's performances. Paper Presented at the Energy Procedia, Vol. 148, pp. 138-145.

Dempsey M. (2006). Dymola for multi-engineering modelling and simulation. 2006 IEEE Vehicle Power and Propulsion Conference.

Dimitriou V., Firth S. K., Hassan T. M., Kane T., Fouchal F. (2014). Developing suitable thermal models for domestic buildings with Smart Home equipment. Proceedings of the 2014 Building Simulation and Optimization Conference.

Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the energy performance of buildings; The European Parliament and of the Council: Brussels, Belgium, 2010.

Dong B., Lam K. P. (2014). A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting. Building Simulation, Vol. 7, No. 1, pp. 89-106.

Economidou M., Atanasiu B., Despret C., Maio J., Nolte I., Rapf O. (2011). Europe’s buildings under the microscope. A Country-by-country review of the energy performance of buildings, Brussels, Belgium, Vol.132.

EU Commission, EU-Commission. (2010). Energy 2020: A strategy for competitive, sustainable and secure energy. Publications Office of the European Union: Luxembourg, Vol. 639.

Ferroukhi M. Y., Djedjig R., Limam K., Belarbi R. (2016). Hygrothermal behavior modeling of the hygroscopic envelopes of buildings: A dynamic co-simulation approach. Build Simul, Vol. 9, No. 5, pp. 501-512.

Ferroukhi M. Y., Djedjig R., Limam K., Belarbi R., Abahri K. (2015). Effect of coupled heat, air and moisture transfers modeling in the wall on the hygrothermal behavior of buildings. Energy Procedia, Vol. 78, pp. 2584-2589.

Fritzson P. (2010). Principles of object-oriented modeling and simulation with Modelica 2.1. 2nd ed.; John Wiley & Sons, New Jersey, USA.

Gagliardo S., Giannini F., Monti M., Pedrielli G., Terkaj W., Sacco M., Ghellere M., Salamone F. (2015). An ontology-based framework for sustainable factories. Computer-Aided Design and Applications, Vol. 12, pp. 2, 198-207.

Guazzi G., Bellazzi A., Meroni I., Magrini A. (2017). Refurbishment design through cost-optimal methodology: The case study of a social housing in the northern Italy. International Journal of Heat and Tecnology, Vol. 35.

Harish V. S. K. V., Kumar A. (2016). A review on modeling and simulation of building energy systems. Renewable and Sustainable Energy Reviews, Vol. 56, pp. 1272-1292.

Kaderják P. (2012). How to refurbish all buildings by 2050. Think, Firenze.

Killian M., Kozek M. (2016). Ten questions concerning model predictive control for energy efficient buildings. Building and Environment, Vol. 105, pp. 403-412.

Künzel H. M., Holm A., Zirkelbach D., Karagiozis A. N. (2005). Simulation of indoor temperature and humidity conditions including hygrothermal interactions with the building envelope. Solar Energy, Vol. 78, No. 4, pp. 554-561.

Li X., Wen J. (2014). Review of building energy modeling for control and operation. Renewable and Sustainable Energy Reviews, Vol. 37, pp. 517-537.

Linda R. P. (1983). A description of DASSL: A differential/algebraic equation solver. In R.S. Stepleman, ed., Scientific Computing, pp. 65–68. North–Holland, Amsterdam, The Netherlands.

Liu X., Chen G., Chen Y. (2016). Modeling of the transient heat, air and moisture transfer in building walls. Hunan Daxue Xuebao, Vol. 43, No. 1, pp. 152-156.

Magrini A., Lazzari S., Marenco L., Guazzi G. (2017). A procedure to evaluate the most suitable integrated solutions for increasing energy performance of the building’s envelope, avoiding moisture problems. International Journal of Heat and Tecnology, Vol. 35.

Moore B. J., Fisher D. S. (2003). Pump differential pressure setpoint reset based on chilled water valve position. ASHRAE Transactions, Vol. 109, No. 1, pp. 373-379.

Nassif N., Kajl S., Sabourin R. (2005). Optimization of HVAC control system strategy using two-objective genetic algorithm. HVAC & R Research, Vol. 11, pp. 459–486.

Nespoli L., Medici V., Rudel R. (2015). Grey-box system identification of building thermal dynamics using only smart meter and air temperature data. In: 14th International Conference of the International Building Performance Simulation Association.

Otter M., Elmqvist H., Cellier F. E. (1996). Modeling of multibody systems with the object-oriented modeling language Dymola. Nonlinear Dynamics, Vol. 9, No. 1-2, pp. 91-112.

Privara S., Cigler J., Váňa Z., Oldewurtel F., Sagerschnig C., Žáčeková E. (2013). Building modeling as a crucial part for building predictive control. Energy and Buildings, Vol. 56, pp. 8-22.

Qin S. J., Badgwell T. A. (1997). An overview of industrial model predictive control technology. In AIChE Symposium Series, Vol. 93, No. 316, pp. 232-256. American Institute of Chemical Engineers, New York, USA.

Riederer P. (2005). Matlab/Simulink for building and HVAC simulation-State of the art. In Ninth International IBPSA Conference, pp. 1019-1026.

Salamone F., Belussi L., Danza L., Ghellere M., Meroni I. (2015). An open source low-cost wireless control system for a forced circulation solar plant. Sensors, Vol. 15, pp. 27990-28004.

Salamone F., Belussi L., Danza L., Ghellere M., Meroni I. (2016). An open source “smart lamp” for the optimization of plant systems and thermal comfort of offices. Sensors, Vol. 16, pp. 338.

Salamone F., Belussi L., Danza L., Ghellere M., Meroni I. (2016). Integration of a do it yourself hardware in a lighting device for the management of thermal comfort and energy use. Energy Procedia, Vol. 101, pp. 161-168.

Salamone F., Belussi L., Danza L., Ghellere M., Meroni I. (2015). Design and development of nEMoS, an all-in-one, low-cost, web-connected and 3D-printed device for environmental analysis. Sensors, Vol. 15, No. 6, pp. 13012-13027.

Salamone F., Danza L., Meroni I., Pollastro M. (2017). A low-cost environmental monitoring system: How to prevent systematic errors in the design phase through the combined use of additive manufacturing and thermographic techniques. Sensors (Switzerland), Vol. 17, No. 4.

Terkaj W., Danza L., Devitofrancesco A., Gagliardo S., Ghellere M., Giannini F., Monti M., Pedrielli G., Sacco M., Salamone F. (2014). A semantic framework for sustainable factories. Procedia CIRP, Vol. 17, pp. 547-552.

Tummescheit H. (2002). Design and implementation of object-oriented model libraries using modelica. PhD Theses (Doctoral dissertation, Lund University).

Van den Boom T. J., Backx T. C. P. M. (2010). Model predictive control. DISC Course, Lecture Notes.

Wang H., Zhai Z. J. (2016). Advances in building simulation and computational techniques: A review between 1987 and 2014. Energy and Buildings, Vol. 128, pp. 319-335.

Zhang Y., Hanby V. I. (2006). Model-based control of renewable energy systems in buildings. HVAC & R Research, Vol. 12, pp. 577–598.

Zong Y., Böning G. M., Santos R. M., You S., Hu J., Han X. (2016). Challenges of implementing economic model predictive control strategy for buildings interacting with smart energy systems. Applied Thermal Engineering, Vol. 114, pp. 1476-1486.