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

Abstract

In order to plan and manage low-carbon investments in wide real estate assets, in this research, a strategic approach is developed to act on building stocks as a whole, with the aim of overcoming the single-building perspective and identifying the energy retrofit level leading to the maximum possible benefit. It is shown how artificial intelligence (AI) and optimization computing are essential to the creation of the decision-making process. In fact, energy improvement consists of an optimization 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, optimization, and risk analysis. This target is achieved by means of Artificial Neural Networks (ANNs) for energy consumption assessment, an Analytic Hierarchy Process for energy retrofit compatibility assessment, and an evolutionary optimization algorithm for the achievement of the optimal configuration of intervention on the stock, maximizing the energy and economic performance of the investment. The proposed procedure is validated on the case study of a building asset located in Northern Italy. Since the developed model relies on AI-based algorithms, it has a consequent limitation: the developed ANNs can work only for the building types, occupation profiles and climatic areas that were used in the training phase. In further development of this research, the aim will be to expand the generalization properties of the forecasting tool.

Keywords

• artificial neural networks
• artificial intelligence
• machine learning
• optimization
• energy retrofit
• buildings