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A Decision Making Technique to Optimize a Buildings’ Stock Energy Efficiency

handle: 11587/421359
This paper focuses on applying multicriteria decision making tools to determine an optimal energy retrofit plan for a portfolio of buildings. We present a two-step decision making technique employing a multiobjective optimization algorithm followed by a multiattribute ranking procedure. The method aims at deciding, in an integrated way, the optimal energy retrofit plan for a whole stock of buildings, optimizing efficiency, sustainability, and comfort, while effectively allocating the available financial resources to the buildings. The proposed methodology is applied to a real stock of public buildings in Bari, Italy. The obtained results demonstrate that the approach effectively supports the city governance in making decisions for the optimal management of the buildings’ energy efficiency.
- Polytechnic University of Bari Italy
- University of Salento Italy
- Polytechnic University of Bari Italy
- Instituto Politécnico Nacional Mexico
Building management, energy efficiency, multiattribute analysis, multicriteria decision making, multiobjective optimization (MOO), optimization algorithms
Building management, energy efficiency, multiattribute analysis, multicriteria decision making, multiobjective optimization (MOO), optimization algorithms
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).52 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
