In order to plan and manage low-carbon investments in wide real estate assets, a strategic ap-proach is developed in this research to act on building stocks as a whole, with the aim of over-coming the single-building perspective and identify the energy retrofit level leading to the maximum possible benefit. It is shown how artificial intelligence and optimisation computing are here essential for the creation of the decision-making process. In fact, energy improvement consists in an optimisation problem in which conflicting objectives and constraints are balanced, and several techniques are integrated to achieve a unified result, including machine learning, economics, building energy simulation, computer programming, optimisation, and risk analysis.

Artificial Intelligence and Optimization Computing to Lead Energy Retrofit Programs in Complex Real Estate Investments

Gabrielli, Laura
Secondo
;
2023

Abstract

In order to plan and manage low-carbon investments in wide real estate assets, a strategic ap-proach is developed in this research to act on building stocks as a whole, with the aim of over-coming the single-building perspective and identify the energy retrofit level leading to the maximum possible benefit. It is shown how artificial intelligence and optimisation computing are here essential for the creation of the decision-making process. In fact, energy improvement consists in an optimisation problem in which conflicting objectives and constraints are balanced, and several techniques are integrated to achieve a unified result, including machine learning, economics, building energy simulation, computer programming, optimisation, and risk analysis.
2023
Artificial Neural Networks, Artificial Intelligence, Machine Learning, Optimization, Energy Retrofit, Buildings, Real Estate
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2568310
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