Review on stochastic modeling methods for building stock energy prediction

Lim, Hyunwoo; Zhai, Zhiqiang

doi:10.1007/s12273-017-0383-y

Cited by 91 publications

(60 citation statements)

References 95 publications

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“…Other relevant issues to be considered are Monte Carlo simulation methods to test the robustness of models' estimates with respect to variable operational conditions (Cecconi et al, 2017) and Bayesian analysis as an extension of conventional regression paradigms (Li et al, 2016). In particular, Bayesian analysis can be used to reconstruct built environment data (Booth et al, 2013;Zhao et al, 2016;Lim and Zhai, 2017), considering the hierarchical data structure outlined in Hierarchical Structure of Building Energy Modelling Data. Further, regression-based approaches could become suitable for projections about energy consumption in future climate change scenarios (Jentsch et al, 2008;Jentsch et al, 2013;Bravo Dias et al, 2020) and to create load profiles when designing decentralized energy systems from buildings (Stadler et al, 2018) up to community scales (Adhikari et al, 2012a;Orehounig et al, 2014;Orehounig et al, 2015), also using clustering techniques to identify typical (recurrent) operational conditions.…”

Section: Further Researchmentioning

confidence: 99%

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

2020

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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.

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Section: Further Researchmentioning

confidence: 99%

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

2020

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“…Archetypes and reference buildings are usually classified according to their occupancy type and contain typical occupant behavior properties. Often, a building occupancy type is primarily distinguished by its occupant presence schedule [35].…”

Section: Occupant Behavior In Urban-scale Building Energy Modelsmentioning

confidence: 99%

“…Recent publications point out that appropriate occupant behavior models and the impact of occupant behavior on energy use at various temporal and spatial resolutions have to be further studied [35,18]. This work aims to be a contribution towards research on occupant behavior models for urban building energy simulations.…”

Section: Occupant Behavior In Urban-scale Building Energy Modelsmentioning

confidence: 99%

A review on occupant behavior in urban building energy models

Happle

Fonseca

Schlueter

2018

Energy and Buildings

160

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Urban building energy models aspire to become key planning tools for the holistic optimization of buildings, urban design, and energy systems in neighborhoods and districts. The energy demand of buildings is largely influenced by the behavior of the occupants. The insufficient consideration of occupant behavior is one of the causes to the "performance gap" in buildings -the difference between the simulated and the actually observed energy consumption. On the urban-scale impacts of different occupant behavior modeling approaches onto the various purposes of urban building energy models are still largely unknown. Research shows that the inappropriate choice of occupant behavior model could result in oversized district energy systems, leading to over-investment and low operational efficiency. This work therefore reviews urban building energy models in terms of their occupant behavior modeling approaches. Three categories of approaches are established and discussed: (1) deterministic space-based approaches, (2) stochastic space-based approaches, and (3) stochastic person-based approaches. They are further assessed in terms of their strategy to consider diversity in occupant behavior. Stochastic models, especially stochastic person-based models, seem to be superior to deterministic models. However, there are no stochastic models available yet that can be used for case studies of mixed-districts, comprising buildings of various occupancy types. In the reviewed urban-scale approaches, only single-use residential or office districts are modeled with stochastic techniques. However, people interact with various buildings on a daily basis. Their activities relate to their presence in different spaces at the urban-scale and to their use of appliances in those spaces. Their individual levels of comfort and behavioral patterns govern the control actions towards building systems. Therefore, a novel activity-based multi-agent approach for urban occupant behavior modeling is proposed as alternative to current approaches.Keywords: urban building energy model, energy-related occupant behavior, occupancy, occupant behavior model IntroductionUrban areas house more than half of the global population and cities are responsible for more than 70% of the global carbon dioxide emissions. At the same time, cities are crucial actors in the transition towards sustainable energy Email address: happle@arch.ethz.ch (Gabriel Happle)

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“…Recent progress in building efficiency impact modeling can be grouped into top-down and bottom-up studies (Lim and Zhai 2017b). In top-down studies, historical relationships are derived between aggregate-level energy use and macro-economic indicators (e.g., gross domestic product, price indices), climatic conditions, appliance ownership, and housing stock turnover rates.…”

Section: Modeling the National Or Regional Impacts Of Building Energymentioning

confidence: 99%