Balancing Energy Efficiency with Indoor Comfort Using Smart Control Agents: A Simulative Case Study

Iakovos T. Michailidis, Roozbeh Sangi, Panagiotis Michailidis, Thomas Schild, Johannes Fuetterer, Dirk Mueller and Elias B. Kosmatopoulos
Additional contact information
Iakovos T. Michailidis: Information Technologies Institute (I.T.I.), Centre for Research & Technology—Hellas (CE.R.T.H.), 57001 Thessaloniki, Greece
Roozbeh Sangi: Institute for Energy Efficient Buildings and Indoor Climate, E.ON. Energy Research Center, RWTH Aachen, 52074 Aachen, Germany
Panagiotis Michailidis: Information Technologies Institute (I.T.I.), Centre for Research & Technology—Hellas (CE.R.T.H.), 57001 Thessaloniki, Greece
Thomas Schild: Drees & Sommer Advanced Building Technologies GmbH, 70569 Stuttgart, Germany
Johannes Fuetterer: Institute for Energy Efficient Buildings and Indoor Climate, E.ON. Energy Research Center, RWTH Aachen, 52074 Aachen, Germany
Dirk Mueller: Institute for Energy Efficient Buildings and Indoor Climate, E.ON. Energy Research Center, RWTH Aachen, 52074 Aachen, Germany
Elias B. Kosmatopoulos: Information Technologies Institute (I.T.I.), Centre for Research & Technology—Hellas (CE.R.T.H.), 57001 Thessaloniki, Greece

Energies, 2020, vol. 13, issue 23, 1-28

Abstract: Modern literature exhibits numerous centralized control approaches—event-based or model assisted—for tackling poor energy performance in buildings. Unfortunately, even novel building optimization and control (BOC) strategies commonly suffer from complexity and scalability issues as well as uncertain behavior as concerns large-scale building ecosystems—a fact that hinders their practical compatibility and broader applicability. Moreover, decentralized optimization and control approaches trying to resolve scalability and complexity issues have also been proposed in literature. Those approaches usually suffer from modeling issues, utilizing an analytically available formula for the overall performance index. Motivated by the complications in existing strategies for BOC applications, a novel, decentralized, optimization and control approach—referred to as Local for Global Parameterized Cognitive Adaptive Optimization (L4GPCAO)—has been extensively evaluated in a simulative environment, contrary to previous constrained real-life studies. The current study utilizes an elaborate simulative environment for evaluating the efficiency of L4GPCAO; extensive simulation tests exposed the efficiency of L4GPCAO compared to the already evaluated centralized optimization strategy (PCAO) and the commercial control strategy that is adopted in the BOC practice (common reference case). L4GPCAO achieved a quite similar performance in comparison to PCAO (with 25% less control parameters at a local scale), while both PCAO and L4GPCAO significantly outperformed the reference BOC practice.

Keywords: building energy systems; building control systems; distributed building optimization and control; centralized building optimization and control; energy-sustainable buildings; Modelica-based test bed (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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