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A Building Energy Management System Based on an Equivalent Electric Circuit Model

Author

Listed:
  • Giovanni Bianco

    (Department of Naval, Electrical, Electronic and Telecommunication Engineering, University of Genoa, Savona Campus, Via Magliotto 2, 17100 Savona, Italy
    Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Savona Campus, Via Magliotto 2, 17100 Savona, Italy)

  • Stefano Bracco

    (Department of Naval, Electrical, Electronic and Telecommunication Engineering, University of Genoa, Savona Campus, Via Magliotto 2, 17100 Savona, Italy)

  • Federico Delfino

    (Department of Naval, Electrical, Electronic and Telecommunication Engineering, University of Genoa, Savona Campus, Via Magliotto 2, 17100 Savona, Italy)

  • Lorenzo Gambelli

    (Department of Naval, Electrical, Electronic and Telecommunication Engineering, University of Genoa, Savona Campus, Via Magliotto 2, 17100 Savona, Italy)

  • Michela Robba

    (Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Savona Campus, Via Magliotto 2, 17100 Savona, Italy)

  • Mansueto Rossi

    (Department of Naval, Electrical, Electronic and Telecommunication Engineering, University of Genoa, Savona Campus, Via Magliotto 2, 17100 Savona, Italy)

Abstract

In recent decades, many EU and national regulations have been issued in order to increase the energy efficiency in different sectors and, consequently, to reduce environmental pollution. In the building sector, energy efficiency interventions are usually based on the use of innovative insulated materials and on the installation of cogeneration and tri-generation units, as well as solar technologies. New and retrofitted buildings are more and more commonly being called “smart buildings”, since they are characterized by the installation of electric and thermal power generation units, energy storage systems, and flexible loads; the presence of such technologies determines the necessity of installing Building Energy Management Systems (BEMSs), which are used to optimally manage their operation. The present paper proposes a BEMS for a smart building, equipped with plants based on renewables (photovoltaics, solar thermal panels, and geothermal heat pump), where the heating and cooling demand are satisfied by a Heating, Ventilation and Air Conditioning System (HVAC) fed by a geothermal heat pump. The developed BEMS is composed of two different modules: an optimization tool used to optimally manage the HVAC plant, in order to guarantee a desired level of comfort inside rooms, and a simulation tool, based on an equivalent electric circuit model and used to evaluate the thermal dynamic behavior of the building. The paper describes the two modules and shows the main results of the validation phase that has been conducted on a real test-case represented by the Smart Energy Building (SEB) located at the Savona Campus of the University of Genoa, Italy.

Suggested Citation

  • Giovanni Bianco & Stefano Bracco & Federico Delfino & Lorenzo Gambelli & Michela Robba & Mansueto Rossi, 2020. "A Building Energy Management System Based on an Equivalent Electric Circuit Model," Energies, MDPI, vol. 13(7), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1689-:d:340865
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    References listed on IDEAS

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    1. De Coninck, Roel & Helsen, Lieve, 2016. "Quantification of flexibility in buildings by cost curves – Methodology and application," Applied Energy, Elsevier, vol. 162(C), pages 653-665.
    2. Tang, Rui & Wang, Shengwei, 2019. "Model predictive control for thermal energy storage and thermal comfort optimization of building demand response in smart grids," Applied Energy, Elsevier, vol. 242(C), pages 873-882.
    3. Nam-Kyu Kim & Myung-Hyun Shim & Dongjun Won, 2018. "Building Energy Management Strategy Using an HVAC System and Energy Storage System," Energies, MDPI, vol. 11(10), pages 1-15, October.
    4. Abhinandana Boodi & Karim Beddiar & Malek Benamour & Yassine Amirat & Mohamed Benbouzid, 2018. "Intelligent Systems for Building Energy and Occupant Comfort Optimization: A State of the Art Review and Recommendations," Energies, MDPI, vol. 11(10), pages 1-26, September.
    5. Ji, Ying & Xu, Peng & Duan, Pengfei & Lu, Xing, 2016. "Estimating hourly cooling load in commercial buildings using a thermal network model and electricity submetering data," Applied Energy, Elsevier, vol. 169(C), pages 309-323.
    6. Stefano Bracco & Federico Delfino & Paola Laiolo & Andrea Morini, 2018. "Planning & Open-Air Demonstrating Smart City Sustainable Districts," Sustainability, MDPI, vol. 10(12), pages 1-14, December.
    7. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    8. Djongyang, Noël & Tchinda, René & Njomo, Donatien, 2010. "Thermal comfort: A review paper," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2626-2640, December.
    9. Li, Xiwang & Malkawi, Ali, 2016. "Multi-objective optimization for thermal mass model predictive control in small and medium size commercial buildings under summer weather conditions," Energy, Elsevier, vol. 112(C), pages 1194-1206.
    10. Pouria Bahramnia & Seyyed Mohammad Hosseini Rostami & Jin Wang & Gwang-jun Kim, 2019. "Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control," Energies, MDPI, vol. 12(24), pages 1-24, December.
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