Linking Design and Operation Phase Energy Performance Analysis Through Regression-Based Approaches

Massimiliano Manfren,Lamberto Tronchin,Benedetto Nastasi

doi:10.3389/fenrg.2020.557649

Massimiliano Manfren, Lamberto Tronchin + Show 1 more

Open Access

https://doi.org/10.3389/fenrg.2020.557649

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Abstract

The reduction of energy usage and environmental impact of the built environment and construction industry is crucial for sustainability on a global scale. We are working towards an increased commitment towards resource efficiency in the built environment and to the growth of innovative businesses following circular economy principles. The conceptualization of change is a relevant part of energy and sustainability transitions research, which is 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 emissions 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 use open science principles in energy research. In this paper, we analyse the topic of linking design and operation phase’s 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 in developing data-driven models that could enhance the possibility of learning from feedback and reconstructing building stock data at multiple levels.

Highlights

It is widely acknowledged that a lower environmental impact from the construction industry and built environment is crucial for sustainability and that this problem has to be tackled on a global scale (Berardi, 2017)
The energy modelling research community at present is emphasizing the fundamental importance of open energy data and models (Pfenninger et al, 2017; Pfenninger et al, 2018), and we can envision an evolution towards systems of models (Bollinger et al, 2018) created to tackle fundamental problems in energy transitions, eventually exploiting soft-linking approaches (Deane et al, 2012; Dominkovicet al., 2020)
The possibility to exploit an approximated physical interpretation of regression model structure could greatly enhance the interpretability and explainability of data-driven methods, learning from feedback to enhance the performance of both single technologies and systems

Summary

INTRODUCTION

It is widely acknowledged that a lower environmental impact from the construction industry and built environment is crucial for sustainability and that this problem has to be tackled on a global scale (Berardi, 2017). A conceptualization is proposed by Thuesen et al (2016), identifying three generic knowledge domains: project, product, and service Methods such as life cycle assessment are fundamental for the development of innovative economic paradigms, such as Circular Economy, in the built environment (Pomponi and Moncaster, 2017), but the assessment of the sustainability of products and practices through life cycle assessment is itself a critical issue. Energy transition strategies have to address complementarities (Markard and Hoffmann, 2016) which are crucial for the co-evolution of built environment and energy infrastructures (Junker et al, 2018; Dominkovicet al., 2020) In this framework, empirically grounded and tested methods that can help in standardizing the way energy consumption data are measured, processed, and reported are valuable, because they can provide reliable evidence, inform policies, and support decision-making processes adequately. Some introductory examples in this sense will be given, indicating the motivation for the review work

BACKGROUND

CONCLUSION