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Adaptive building energy management with multiple commodities and flexible evolutionary optimization

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  • Mauser, Ingo
  • Müller, Jan
  • Allerding, Florian
  • Schmeck, Hartmut

Abstract

To enable the efficient utilization of energy carriers and the successful integration of renewable energies into energy systems, building energy management systems (BEMS) are inevitable. In this article, we present a modular BEMS and its customizable architecture that enable a flexible approach towards the optimization of building operation. The system is capable of handling the energy flows in the building and across all energy carriers as well as the interdependencies between devices, while keeping a unitized approach towards devices and the optimization of their operation. Evaluations in realistic scenarios show the ability of the BEMS to increase energy efficiency, self-consumption, and self-sufficiency as well as to reduce energy consumption and costs by an improved scheduling of the devices that considers all energy carriers in buildings as well as their interdependencies.

Suggested Citation

  • Mauser, Ingo & Müller, Jan & Allerding, Florian & Schmeck, Hartmut, 2016. "Adaptive building energy management with multiple commodities and flexible evolutionary optimization," Renewable Energy, Elsevier, vol. 87(P2), pages 911-921.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p2:p:911-921
    DOI: 10.1016/j.renene.2015.09.003
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    2. Katsaprakakis, Dimitris Al. & Georgila, Klairi & Zidianakis, Georgios & Michopoulos, Apostolos & Psarras, Nikolaos & Christakis, Dimitris G. & Condaxakis, Constantinos & Kanouras, Spyros, 2017. "Energy upgrading of buildings. A holistic approach for the Natural History Museum of Crete, Greece," Renewable Energy, Elsevier, vol. 114(PB), pages 1306-1332.
    3. Fu, Xueqian & Zhang, Xiurong, 2019. "Estimation of building energy consumption using weather information derived from photovoltaic power plants," Renewable Energy, Elsevier, vol. 130(C), pages 130-138.
    4. Ahmad, Tanveer & Chen, Huanxin, 2018. "Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment," Energy, Elsevier, vol. 160(C), pages 1008-1020.
    5. Antonio Piacentino & Roberto Gallea & Pietro Catrini & Fabio Cardona & Domenico Panno, 2016. "On the Reliability of Optimization Results for Trigeneration Systems in Buildings, in the Presence of Price Uncertainties and Erroneous Load Estimation," Energies, MDPI, vol. 9(12), pages 1-31, December.

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