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

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.

Long-Term Techno-Economic Performance Monitoring to Promote Built Environment Decarbonisation and Digital Transformation—A Case Study

“…The study's goal is to test the applicability of the modelling approach proposed as a screening tool for understanding the impact of energy efficiency measures and monitor energy related costs. The modelling approach can be seen as an extension of other regression-based approached for energy monitoring and Measurement and Verification (M & V), reviewed in recent literature [16,17]. While not as accurate and reliable as techniques for M & V [20,26,27], this regression-based approach can be nonetheless an inexpensive tool to track performance in time using cost data that are normally available in a digital format (e.g., for accounting purpose, as explained before) and may serve as a screening tool to assess the impact of efficiency measures across multiple buildings with similar characteristics when more contextual information is available.…”

“…In this framework, energy modelling and energy analytics using interpretable ML techniques [16] can contribute to the construction sector's sustainability transition process by employing (temporally and spatially) scalable techniques that can provide insights and feedback on performance. Furthermore, they can ensure a certain level of continuity in the way performance analysis is conducted from design to operation [17]; this aspect is particularly critical in a "circular" economy perspective. Finally, they can provide a physical interpretation of the quantities [18], contributing to the further development of harmonised and standardised approaches [19,20].…”

Energy Modelling and Analytics in the Built Environment—A Review of Their Role for Energy Transitions in the Construction Sector

“…Automated model selection techniques [119,127] can be applied as well to compare the performance of multiple modelling options, using statistical KPIs representing their "goodness of fit". From an analytical perspective, it is important to be able to connect both the design and the operation phase analysis [128,129] in order to ensure consistency in the use of energy performance analysis techniques over the different phases of the life cycle [130]. In this way reliable limits for performance measured or estimated [131] can be produced and used against benchmarks, allowing a continuous improvement process (i.e., Plan Do Check Act is one of the key principles of Energy Management Systems [132]).…”

A New Generation of Thermal Energy Benchmarks for University Buildings

“…Adopting linear regression model [41], the energy demand prediction results of the model were assessed versus the actual energy demand (Figure 5). R-squared (R 2 ) is a statistical measure that represents the proportion of the variance for a dependent variable that is explained by the independent variables in a regression model.…”