A Short review on the use of renewable energies and model predictive control in buildings

Abstract

This short review is based on an overview of the most recent works of the literature related to climatization in buildings. A total number of 40 relevant papers that have been published in the last years in prestigious international journals have been reviewed with the aim of showing the current state of the art in this field. It is very important as the new European regulations that will be applied in the next years in the construction of buildings, aiming to achieve nearly-Zero Energy Buildings (nZEBs), will require a multidisciplinary work in the different areas that affect the design of buildings. For this reason, it is relevant the envelope, the user behavior, the Heating, Ventilation and Air Conditioning applied (HVAC) and the influence of the meteorological conditions, among others. But apart from this, it would be very interesting any other alternative which helped to reach these targets. This article proposes the possibility of using Model Predictive Control (MPC) besides to Renewable Energies in order to optimize the energy management of air-conditioning in public and office buildings through a radiant floor by solar system and high-performance heat pump systems for their heating / cooling. In this paper, a short review is initially exposed where the different "Input data and information" are analyzed, to end up proposing an Operative for a Predictive Control using these Renewable Energies.

Keywords

• Building energy systems,Energy-efficient building,Model predictive control

References

1. United Nations Environment Programme, “Why buildings”, 2015. Available: http://staging.unep.org/sbci/AboutSBCI/Background.asp (accessed on 24 April 2017).
2. European Commission, “EU Energy in Figures. Statistical Pocketbook 2014, 2015 and 2016”. Available: https://ec.europa.eu/energy/en/data-analysis/energy-statistical-pocketbook (accessed on 24 April 2017).
3. U.S. Energy Information Administration, “Commercial Buildings Energy Consumption Survey 2012” and “Monthly Energy Review 2017”. Available: https://www.eia.gov/consumption/commercial/reports.php/.
4. Ruparathna, R., Hewage, K., Sadiq, R., “Improving the energy efficiency of the existing building stock: A critical review of commercial and institutional buildings”, Renewable and Sustainable Energy Reviews, 53, 1032-1045 (2015).
5. Harish, V.S.K.V., Kumar, A., “A review on modeling and simulation of building energy systems”, Renewable and Sustainable Energy Reviews, 56, 1272-1292, (2015).
6. Roberts, S., “Altering existing buildings in the UK”, Energy Policy, 36, 4482-4486 (2008).
7. Chandel, S.S., Sharma A., Marwaha B.M., “Review of energy efficiency initiatives and regulations for residential buildings in India”, Renewable and Sustainable Energy Reviews, 54, 1443-1458 (2016).
8. Santos-Herrero, J.M., Lopez-Guede, J.M., Flores, I., Sala, J.M., “An ongoing review on building energy efficiency improvement systems”, 4. European Conference on Renewable Energy Systems, Istanbul, 28-31 August 2016.

9. Kneifel, J., “Life-cycle carbon and cost analysis of energy efficiency measures in new commercial buildings”, Energy and Buildings, 42, 333-340 (2010).
10. García, C.E., Prett, D.M., Morari, M., “Model predictive control: Theory and practice—A survey”, Automatica, 25, 335-348 (1989).
11. Cho, S.H., Zaheer-uddin, M., “Predictive control of intermittently operated radiant floor heating systems”, Energy Conversion and Management, 44, 1333-1342 (2003).
12. Oldewurtel, F., Parisio, A., Jones, C., Morari, M., Gyalistras, D., Gwerder, M., Stauch, V., Lehmann, B., Wirth, K., “Energy efficient building climate control using stochastic model predictive control and weather predictions”, American Control Conference, 5100–5105, 30 June – 2 July 2010.
13. Oldewurtel, F., “Stochastic Model Predictive Control for Energy Efficient Building Climate Control”, Ph.D. Dissertation ETH Zurich - No. 19908 (2011).
14. Oldewurtel, F., Parisio, A., Jones, C., Gyalistras, D., Gwerder, M., Stauch, V., Lehmann, B., Morari, M., “Use of model predictive control and weather forecasts for energy efficient building climate control”, Energy and Buildings, 45, 15-27 (2011).
15. Široký, J., Oldewurtel, F., Cigler, J., Prívara, S., “Experimental analysis of model predictive control for an energy efficient building heating system”, Applied Energy, 88, 3079-3087 (2011).
16. Cigler, J., Gyalistras, D., Široký, J., Tiet, V-N., Ferkla, L., “Beyond theory: the challenge of implementing Model Predictive Control in buildings”, CLIMA 2013: 11th REHVA World Congress & 8th International Conference on IAQVEC, Prague, 16-19 June 2013
17. Cigler, J, “Model Predictive Control for Buildings”, Ph.D. dissertation Czech Technical University in Prague Faculty of Electrical Engineering (2013).
18. Fabietti, L., “Control of HVAC Systems via Explicit and Implicit MPC: An Experimental Case Study”, Master's Degree Project of the KTH Electrical Engineering - No. XE-EE-RT 2014:006 (2014).
19. Xiwang, L., Wen, J., “Review of building energy modeling for control and operation”, Renewable and Sustainable Energy Reviews, 37, 517-537 (2014).
20. De Coninck, R., Magnusson, F., Akesson, J., Helsen, L., “Toolbox for development and validation of grey-box building models for forecasting and control”, Journal of Building Performance Simulation, 9(3) (2015).
21. De Coninck, R., Helsen, L., “Practical implementation and evaluation of model predictive control for an office building in Brussels”, Energy and Buildings, 111, 290-298 (2016).
22. Carrascal, E., Garrido, I., Garrido, A.J., Sala, J.M, “Optimization of the Heating System Use in Aged Public Buildings via Model Predictive Control”, Energies, 9, 251 (2016).
23. Ascione, F., Bianco, N., De Stasio, C., Mauro, G.M., Vanoli, G.P., “Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort”, Energy and Buildings, 111, 131-144 (2015).
24. Hu, Q., Oldewurtel, F., Balandat, M., Vrettos, E., Zhou, D., Tomlin, C.J., “Building Model Identification during Regular Operation – Empirical Results and Challenges”, IEEE American Control Conference, 6–8 July 2016.
25. Sturzenegger, D., Gyalistras, D., Morari, M., Smith, R.S, “Model Predictive Climate Control of a Swiss Office Building: Implementation, Results, and Cost-Benefit Analysis”, Control Systems Technology, 24(1) (2015).
26. Vaccarini, M., Giretti, A., Tolve, L.C, Casals, M., “Model predictive energy control of ventilation for underground stations”, Energy and Buildings, 116, 326-340 (2016).
27. Marvuglia, A., Messineo, A., Nicolosi, G., “Coupling a neural network temperature predictor and a fuzzy logic controller to perform thermal comfort regulation in an office building”, Building and Environment, 72, 287-299 (2014).
28. Collotta, M., Messineo, A., Nicolosi, G., Pau, G., “A Dynamic Fuzzy Controller to Meet Thermal Comfort by Using Neural Network Forecasted Parameters as the Input”, Energies, 7, 4727-4756 (2014).
29. Dragomir, O.E., Dragomir, F., Stefan, V., Minca, E., “Adaptive neuro-fuzzy inference systems as a strategy for predicting and controling the energy produced from renewable sources”, Energies, 8, 13047-13061, (2015).
30. Ghadi, Y.Y., Rasul, M.G., Khan, M.M.K., “Design and development of advanced fuzzy logic controllers in smart buildings for institutional buildings in subtropical Queensland”, Renewable and Sustainable Energy Reviews, 54, 738-744 (2015).
31. Reena, M., Mathew, A.T., Jacob, L., “Energy Efficient Wireless Networked Building Automation System Controlled by Real Occupancy”, TENCON 2015 - IEEE Region 10 Conference, Macau, 1-4 November 2015.
32. Oldewurtel, F., Sturzenegger, D., Morari, M., “Importance of occupancy information for building climate control”, Applied Energy, 101, 521-532 (2012).
33. Hawila, A.W., Merabtine, A., Troussier, N., Mokraoui, S., Kheiri, A., Laaouatni, A., “Dynamic model validation of the radiant floor heating system based on the object oriented approach”, 4. International Renewable and Sustainable Energy Conference, Marrakech, 14-17 November 2016.
34. Sarbu, I., Sebarchievici, C., “Performance evaluation of radiator and radiant floor heating systems for an office room connected to a ground-coupled heat pump”, Energies, 9, 228 (2016).
35. Ruelens, F., Iacovella, S., Claessens, B.J., Belmans, R., “Learning agent for a heat-pump thermostat with a set-back strategy using model-free reinforcement learning”, Energies, 8, 8300-8318, 2015.
36. Tsai, H.L., “Design and Evaluation of a Photovoltaic / Thermal-Assisted Heat Pump Water Heating System”, Energies, 7, 3319-3338 (2014).
37. Susorova, I., Tabibzadeh, M., Rahman, A., Clack, H.L., Elnimeiri, M., “The effect of geometry factors on fenestration energy performance and energy savings in office buildings”, Energy and Buildings, 57, 6-13 (2013).
38. Lin, H-W., Hong, T., “On variations of space-heating energy use in office buildings”, Applied Energy, 111, 515-528 (2013).
39. Aste, N., Caputo, P., Buzzetti, M., Fattore, M., “Energy efficiency in buildings: What drives the investments? The case of Lombardy Region”, Sustainable Cities and Society, 20, 27-37 (2016).
40. Chung, M.H., Park, J.C., “Development of PCM cool roof system to control urban heat island considering temperate climatic conditions”, Energy and Buildings, 116, 341-348 (2016).