Algorithm for explicit solution to the three parameter linear change-point regression model

Paulus, Mitchell T.

doi:10.1080/23744731.2016.1267998

Cited by 14 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As already mentioned, the methods used in these projects represent an extension of the ones that can be found in M&V protocols [111,112] and technical standards [115][116][117], in which thresholds (expressed as statistical KPIs, representing the "goodness of fit") are given for the acceptability of models as calibrated [118] on measured data. Finally, open software is available [113,119,120] as a basis for further development that can potentially be enabled by open science principles (i.e., transparency and reproducibility of results, among others).…”

Section: Harmonizing Methodologies To Analyse Energy Performancementioning

confidence: 99%

“…Conceptual simplicity is one of their advantages (among others), compared to other meta-modelling techniques [125,126]. 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].…”

Section: Harmonizing Methodologies To Analyse Energy Performancementioning

confidence: 99%

See 1 more Smart Citation

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

2021

View full text Add to dashboard Cite

Decarbonisation and efficiency goals set as a response to global warming issue require appropriate decision-making strategies to promote an effective and timely change in energy systems. Conceptualization of change is a relevant part of energy transitions research today, which aims at enabling radical shifts compatible with societal functions and market mechanisms. In this framework, construction sector can play a relevant role because of its energy and environmental impact. There is, however, the need to move from general instances to specific actions. Open data and open science, digitalization and building data interoperability, together with innovative business models could represent enabling factors to accelerate the process of change. For this reason, built environment research has to address the co-evolution of technologies and human behaviour and the analytical methods used for this purpose should be empirically grounded, transparent, scalable and consistent across different temporal/spatial scales of analysis. These features could potentially enable the emergence of “ecosystems” of applications that, in turn, could translate into projects, products and services for energy transitions in the built environment, proposing innovative business models that can stimulate market competitiveness. For these reasons, in this paper we organize our analysis according to three levels, from general concepts to specific issues. In the first level, we consider the role of building energy modelling at multiple scales. In the second level, we focus on harmonization of methods for energy performance analysis. Finally, in the third level, we consider emerging concepts such as energy flexibility and occupant-centric energy modelling, considering their relation to monitoring systems and automation. The goal of this research is to evaluate the current state of the art and identify key concepts that can encourage further research, addressing both human and technological factors that influence energy performance of buildings.

show abstract

Section: Harmonizing Methodologies To Analyse Energy Performancementioning

confidence: 99%

Section: Harmonizing Methodologies To Analyse Energy Performancementioning

confidence: 99%

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

2021

View full text Add to dashboard Cite

show abstract

“…We already mentioned conceptual simplicity as one of the advantages of this type of model compared to other metamodelling techniques (Manfren et al, 2013). Additionally, given the standard structure of the basic models, automated or partially automated model selection techniques (Paulus et al, 2015;Paulus, 2017) can be applied to compare the performance of multiple modelling options. Clearly, the presence of different operating conditions in time (e.g., different types of operational profiles) may determine the need to cluster operational conditions on a daily basis (Jalori and Reddy, 2015a;Miller et al, 2015;Richard et al, 2017).…”

Section: Regression Models In Operational Phase Analysismentioning

confidence: 99%

“…Temporal Monthly (Abels et al, 2011;Hallinan et al, 2011a, Hallinan et al, 2011bLammers et al, 2011;Server et al, 2011) Daily (Masuda and Claridge, 2012b;Danov et al, 2013;Masuda and Claridge, 2014;Paulus et al, 2015;Hitchin and Knight, 2016;Paulus, 2017) Hourly (Jalori and Reddy, 2015b;Abushakra and Paulus, 2016) Spatial Building fabric heat transfer (Bauwens and Roels, 2014;Erkoreka et al, 2016;Giraldo-Soto et al, 2018;Uriarte et al, 2019) Building energy behaviour (Masuda and Claridge, 2014;Lin and Claridge, 2015;Paulus et al, 2015) Building stock energy behaviour (Meng and Mourshed, 2017;Meng et al, 2020) Community and city scale analysis energy behaviour (Qomi et al, 2016;Pasichnyi et al, 2019) regression-based approaches that could be used to create scalable (temporally and spatially) integrated data analysis workflows from design to operation in buildings. In this sense, we showed how data analysis techniques could be used to evaluate the impact of both technical and human factors, with the aim of reconstructing building stock data at multiple levels.…”

Section: Sub Topic Referencesmentioning

confidence: 99%

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

2020

View full text Add to dashboard Cite

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.

show abstract

“…Nowadays, most computer systems are built for performing general or businessspecific tasks through machine learning technologies. Evidence of their applications can be seen in problem domains such as in the medical field, policy-making [1], fraud detection [2][3][4], and signal processing [5,6]. Different machine learning models, also known as mathematical models, possess different attributes that produce accurate results using prediction calculations when exposed to a given dataset.…”

Section: Introductionmentioning

confidence: 99%

Estimation and Interpretation of Machine Learning Models with Customized Surrogate Model

et al. 2021

View full text Add to dashboard Cite

Machine learning has the potential to predict unseen data and thus improve the productivity and processes of daily life activities. Notwithstanding its adaptiveness, several sensitive applications based on such technology cannot compromise our trust in them; thus, highly accurate machine learning models require reason. Such models are black boxes for end-users. Therefore, the concept of interpretability plays the role if assisting users in a couple of ways. Interpretable models are models that possess the quality of explaining predictions. Different strategies have been proposed for the aforementioned concept but some of these require an excessive amount of effort, lack generalization, are not agnostic and are computationally expensive. Thus, in this work, we propose a strategy that can tackle the aforementioned issues. A surrogate model assisted us in building interpretable models. Moreover, it helped us achieve results with accuracy close to that of the black box model but with less processing time. Thus, the proposed technique is computationally cheaper than traditional methods. The significance of such a novel technique is that data science developers will not have to perform strenuous hands-on activities to undertake feature engineering tasks and end-users will have the graphical-based explanation of complex models in a comprehensive way—consequently building trust in a machine.

show abstract

Algorithm for explicit solution to the three parameter linear change-point regression model

Cited by 14 publications

References 15 publications

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

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

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

Estimation and Interpretation of Machine Learning Models with Customized Surrogate Model

Contact Info

Product

Resources

About