Minelli, Federico (2025) A robust multi-objective optimization and multi-criteria decision-making approach for the analysis of the building-plant system energy, environmental and economic performance. [Tesi di dottorato]

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Abstract

The increasing demand for energy-efficient and sustainable buildings necessitates innovative methodologies that address the complex interplay between energy performance, environmental impacts, and other stakeholder interests. This PhD thesis presents a comprehensive approach that integrates “robust” Multi-Objective Optimization (MOO) and Multi-Criteria Decision-Making (MCDM) techniques, aimed at enhancing the performance of building Heating, Ventilation, and Air Conditioning (HVAC) systems alongside Renewable Energy Sources (RES). The research establishes a theoretical framework that identifies gaps in current methodologies and formulates key research questions guiding the study. The proposed robust MOO and MCDM approach demonstrates its capability to effectively address the uncertainties and conflicts inherent in energy system optimization, emphasizing the importance of “robustness”. In the context of energy systems, robustness refers to the ability to consistently perform well despite uncertainties such as fluctuating energy demands, variable renewable energy sources, and unforeseen disruptions. Additionally, robustness in the decision-making process ensures that the outcomes remain reliable and stable even when different decision-making methods are employed, which can otherwise yield varying results. The proposed comprehensive approach is aimed at ensuring that energy solutions are not only efficient under ideal conditions but also resilient and adaptable to real-world variations and methodological differences. Practical implementations of the methodology are explored through several case studies, including the optimization of Net Zero Energy Buildings and energy retrofits for commercial spaces. These case studies illustrate the applicability of the approach in optimizing energy, environmental, and economic performance while accommodating diverse stakeholder perspectives and conflicting interests. To further facilitate practical application, an open-source digital tool is developed to operationalize the robust optimization and decision-making process. This tool promotes wider accessibility and enables practitioners to make informed decisions regarding building design and retrofitting by providing robust solutions tailored to specific energy performance objectives. The findings of this research can contribute significantly to the fields of building energy management and sustainable design, offering a robust framework that enhances decision-making in the context of complex building energy systems. Furthermore, the implications of this work extend to policymakers and industry stakeholders, providing a pathway towards achieving greater energy efficiency and sustainability in the built environment.

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