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EplusLauncher: An API to Perform Complex EnergyPlus Simulations in MATLAB ® and C#

Author

Listed:
  • Germán Campos Gordillo

    (Aurea Consulting, Sustainable Architecture and Engineering, 20110 Pasaia, Spain)

  • Germán Ramos Ruiz

    (School of Architecture, University of Navarra, 31009 Pamplona, Spain)

  • Yves Stauffer

    (Centre Suisse d’Electronique et de Microtechnique, 2002 Neuchâtel, Switzerland)

  • Stephan Dasen

    (Centre Suisse d’Electronique et de Microtechnique, 2002 Neuchâtel, Switzerland)

  • Carlos Fernández Bandera

    (School of Architecture, University of Navarra, 31009 Pamplona, Spain)

Abstract

There is a growing concern about how to mitigate climate change, in which the production and use of energy has a great impact as one of the largest sources of global greenhouse gases (GHG). Buildings are responsible for a large percentage of these emissions. Therefore, there has been an increase in research in this area, in order to reduce their consumption and increase their efficiency. One of the major simulation programs used in optimization research is EnergyPlus. The purpose of this software is the complete energy simulation of a building, although it lacks tools to analyze its results and, above all, to manage and edit its simulations. For this reason, we developed an application programming interface (API) that serves to merge two areas which are highly demanded by researchers: energy building simulation (using EnergyPlus) and tools for the management and design of research experiments (in this case, MATLAB ® ). The developed API allows the user to perform complex simulations using EnergyPlus in a simple way, as it allows the editing of each simulation and the analysis of the simulation results through MATLAB ® . In addition, it enables the user to simultaneously run multiple simulations, using either all computer core processors or a selection of them (i.e., allowing parallel computing), reducing the simulation time. The API was developed in the C# language, such that it can be used with any software that can import . N E T libraries.

Suggested Citation

  • Germán Campos Gordillo & Germán Ramos Ruiz & Yves Stauffer & Stephan Dasen & Carlos Fernández Bandera, 2020. "EplusLauncher: An API to Perform Complex EnergyPlus Simulations in MATLAB ® and C#," Sustainability, MDPI, vol. 12(2), pages 1-14, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:2:p:672-:d:309669
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    References listed on IDEAS

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    1. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2016. "Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: A new approach to assess cost-optimality," Applied Energy, Elsevier, vol. 174(C), pages 37-68.
    2. Alibabaei, Nima & Fung, Alan S. & Raahemifar, Kaamran & Moghimi, Arash, 2017. "Effects of intelligent strategy planning models on residential HVAC system energy demand and cost during the heating and cooling seasons," Applied Energy, Elsevier, vol. 185(P1), pages 29-43.
    3. Ibrahim, Mohamad & Biwole, Pascal Henry & Wurtz, Etienne & Achard, Patrick, 2014. "Limiting windows offset thermal bridge losses using a new insulating coating," Applied Energy, Elsevier, vol. 123(C), pages 220-231.
    4. Fabrizio Ascione & Nicola Bianco & Rosa Francesca De Masi & Gerardo Maria Mauro & Giuseppe Peter Vanoli, 2015. "Design of the Building Envelope: A Novel Multi-Objective Approach for the Optimization of Energy Performance and Thermal Comfort," Sustainability, MDPI, vol. 7(8), pages 1-28, August.
    5. Pang, Zhihong & O'Neill, Zheng, 2018. "Uncertainty quantification and sensitivity analysis of the domestic hot water usage in hotels," Applied Energy, Elsevier, vol. 232(C), pages 424-442.
    6. Reynolds, Jonathan & Ahmad, Muhammad Waseem & Rezgui, Yacine & Hippolyte, Jean-Laurent, 2019. "Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 699-713.
    7. Rackes, Adams & Melo, Ana Paula & Lamberts, Roberto, 2016. "Naturally comfortable and sustainable: Informed design guidance and performance labeling for passive commercial buildings in hot climates," Applied Energy, Elsevier, vol. 174(C), pages 256-274.
    8. Delgarm, N. & Sajadi, B. & Kowsary, F. & Delgarm, S., 2016. "Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO)," Applied Energy, Elsevier, vol. 170(C), pages 293-303.
    9. Smarra, Francesco & Jain, Achin & de Rubeis, Tullio & Ambrosini, Dario & D’Innocenzo, Alessandro & Mangharam, Rahul, 2018. "Data-driven model predictive control using random forests for building energy optimization and climate control," Applied Energy, Elsevier, vol. 226(C), pages 1252-1272.
    10. Santos, Luis Guilherme Resende & Afshari, Afshin & Norford, Leslie K. & Mao, Jiachen, 2018. "Evaluating approaches for district-wide energy model calibration considering the Urban Heat Island effect," Applied Energy, Elsevier, vol. 215(C), pages 31-40.
    11. Favoino, Fabio & Overend, Mauro & Jin, Qian, 2015. "The optimal thermo-optical properties and energy saving potential of adaptive glazing technologies," Applied Energy, Elsevier, vol. 156(C), pages 1-15.
    12. Bianchini, Gianni & Casini, Marco & Pepe, Daniele & Vicino, Antonio & Zanvettor, Giovanni Gino, 2019. "An integrated model predictive control approach for optimal HVAC and energy storage operation in large-scale buildings," Applied Energy, Elsevier, vol. 240(C), pages 327-340.
    13. Nguyen, Anh-Tuan & Reiter, Sigrid & Rigo, Philippe, 2014. "A review on simulation-based optimization methods applied to building performance analysis," Applied Energy, Elsevier, vol. 113(C), pages 1043-1058.
    14. Favoino, Fabio & Fiorito, Francesco & Cannavale, Alessandro & Ranzi, Gianluca & Overend, Mauro, 2016. "Optimal control and performance of photovoltachromic switchable glazing for building integration in temperate climates," Applied Energy, Elsevier, vol. 178(C), pages 943-961.
    15. Hu, Maomao & Xiao, Fu & Jørgensen, John Bagterp & Wang, Shengwei, 2019. "Frequency control of air conditioners in response to real-time dynamic electricity prices in smart grids," Applied Energy, Elsevier, vol. 242(C), pages 92-106.
    16. Li, Hangxin & Wang, Shengwei & Cheung, Howard, 2018. "Sensitivity analysis of design parameters and optimal design for zero/low energy buildings in subtropical regions," Applied Energy, Elsevier, vol. 228(C), pages 1280-1291.
    17. Tabares-Velasco, Paulo Cesar & Speake, Andrew & Harris, Maxwell & Newman, Alexandra & Vincent, Tyrone & Lanahan, Michael, 2019. "A modeling framework for optimization-based control of a residential building thermostat for time-of-use pricing," Applied Energy, Elsevier, vol. 242(C), pages 1346-1357.
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    Cited by:

    1. Giacomo Chiesa & Francesca Fasano & Paolo Grasso, 2021. "A New Tool for Building Energy Optimization: First Round of Successful Dynamic Model Simulations," Energies, MDPI, vol. 14(19), pages 1-20, October.

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