Linking Design and Operation Phase Energy Performance Analysis Through Regression-Based Approaches - Manfren, Massimiliano; Nastasi, Benedetto; Tronchin, Lamberto (Frontiers in Energy Research, 2020)
The reduction of energy and environmental impact of built environment and construction industry is crucial for sustainability at global scale. We are assisting at an increasing commitment towards resource efficiency in the built environment and to the growth of innovative businesses following circular economy principles. Conceptualization of change is a relevant part of the energy and sustainability transitions research, which are aimed at enabling radical shifts compatible with societal functions. In this framework, building performance has to be considered in a whole life cycle perspective because buildings are long term assets. In a life cycle perspective both operational and embodied energy and carbon emission have to be considered for appropriate comparability and decision making. The application of sustainability assessments of products and practices in the built environment is itself a critical and debatable issue. For this reason, the way energy consumption data are measured, processed and reported has to be progressively standardized in order to enable transparency and consistency of methods at multiple scales (from single buildings up to building stock) and levels of analysis (from individual components up to systems), ideally complementing ongoing research initiatives that are using open science principles in energy research. In this paper, we analyse the topic of linking design and operation phase energy performance analysis through regression-based approaches in buildings, highlighting the hierarchical nature of building energy modelling data. The goal of this research is to review the current state of the art of in order to orient future efforts towards integrated data analysis workflows, from design to operation. In this sense, we show how data analysis techniques can be used to evaluate the impact of both technical and human factors. Finally, we indicate how approximated physical interpretation of regression models can help developing data-driven models that could enhance the possibility to learn from feed-back and reconstruct building stock data at multiple levels.