Maximization of the Smart Readiness Indicator of Buildings Under Budget Constraints

Tristan Emich (), Shiva Faeghi () and Kunibert Lennerts ()
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Tristan Emich: Karlsruhe Institute of Technology
Shiva Faeghi: Karlsruhe Institute of Technology
Kunibert Lennerts: Karlsruhe Institute of Technology

Chapter Chapter 32 in Operations Research Proceedings 2022, 2023, pp 261-269 from Springer

Abstract: Abstract The Smart Readiness Indicator (SRI) is a method proposed by the European Commission which calls for better use of the potential of smart technologies in the building sector. The introduction of the SRI is intended to raise awareness of smart building technologies and make the added value more available for building users, owners, and providers of smart services. The technological smart readiness of buildings can be determined with the SRI assessment method. The method has 54 questions, which are divided into nine domains: heating, domestic hot water, cooling, ventilation, lighting, electricity, electric vehicles, dynamic envelope and monitoring & control. Each question is assessed with up to five different levels, representing incremental levels of technological equipment. These questions form the basis for the calculation of the SRI score. When improving the SRI score of a building, to choose the technologies that will provide the maximum SRI score improvement with a limited budget can be challenging. Therefore, the aim of this paper is to help the decision makers to come up with the correct choices that have the highest impact on the SRI score. The chosen solution method here is a specific non-dominated sorting genetic algorithm (NSGA II) algorithm. The proposed method is then applied to a hypothetical building to demonstrate its applicability and capability. The results show which SRI domains and questions to focus on. This gives future directions regarding choosing technologies to be implemented.

Keywords: Combinatorial optimization; Energy policy and planning; Decision support systems (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-24907-5_32

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