Reconstructing building stock to replicate energy consumption data

Zhao, Fei; Lee, Sang Hoon; Augenbroe, Godfried

doi:10.1016/j.enbuild.2015.10.001

Cited by 42 publications

(22 citation statements)

References 11 publications

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“…Global sensitivity methods assess the significance of input parameters by varying all other model inputs at the same time. Screening methods, such as the EE method [36], are qualified as global methods and can be used to reduce computational cost in high-dimensional or computationally expensive models [37]. EE typically does not characterise the uncertainty according to their complete probability distribution but rather randomly varies inputs by adding proportions and analyse the effects.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Global sensitivity analysis as a support for the generation of simplified building stock energy models

Mastrucci

Pérez‐López

Benetto

et al. 2017

Energy and Buildings

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International audienceBuildings are responsible for 40% of total final energy consumptions in Europe. Numerous bottom-up models were recently developed to support local authorities in assessing the energy consumption of large building stocks and reduction potentials. However, current models rarely consider uncertainty associated to building usage and characteristics within the stock, resulting in potentially biased results. This study presents a generic model simplification approach using uncertainty propagation and stochastic sensitivity analysis to derive fast simplified (surrogate) models to estimate the current building stock energy use for improved urban planning. The methodology includes an engineering-based energy model as input to global sensitivity analysis (GSA) using the elementary effects (EE) screening and Sobol’ method for key parameters identification and regression analysis to derive simplified models for entire building stocks. The application to the housing stock of Esch-sur-Alzette (Luxembourg) showed that the parameters explaining most of the variability in final energy use for heating and domestic hot water are floor area, set-point temperature, external walls U-values, windows and heating system type. Results of the simplified models were validated against measured data and confirmed the validity of the approach for a simple yet robust assessment of the building stock energy use considering uncertainty and variability

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Section: Sensitivity Analysismentioning

confidence: 99%

Global sensitivity analysis as a support for the generation of simplified building stock energy models

Mastrucci

Pérez‐López

Benetto

et al. 2017

Energy and Buildings

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show abstract

“…Zhao et al (2016) argued in his research that the calibrated building parameter distributions should be considered as "best guess" of the real world. Future research should investigate the relationship between the estimated parameter distribution and the actual distribution, which will improve an understanding the energy conservation measures of building stock.…”

Section: Calibration Process and Resultsmentioning

confidence: 99%

“…By considering the occupant factor, they reproduce more realistic EUI distribution for 2,182 apartment units in Korea than a deterministic method. Zhao et al (2016) proposed an approach to replicate an office building stock energy use in Chicago, U.S. from the survey data. This method used a linear inverse problem to derive design parameters of buildings.…”

Section: Stochastic Building Energy Models For Individual Buildingsmentioning

confidence: 99%

Review on stochastic modeling methods for building stock energy prediction

Lim

Zhai

2017

Build. Simul.

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Increasing risks of energy security and greenhouse gas emission due to the growing urbanization trend have prompted the need for urban energy demand prediction and management, in which the building energy consumption is the main cause. This paper reviews the recent advances and state-of-the-art in modeling building stock energy consumption, including both the top-down and bottom-up approaches. The study compares and summarizes the strengths and weaknesses of each primary method. Specific focus has been paid to the bottom-up stochastic engineering modeling methods, which hold sound quantitative theory bases as well as considering uncertain reality conditions. Stochastic building stock energy models account for the uncertainties that are the main limitation in existing building stock models. Discussions are provided regarding the process in the current stochastic building stock energy model. Challenges and possible future directions are examined for the improvement of stochastic building stock energy model.

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“…(1) training data is required for model development, (2) the model is limited to a specific location and building type, and (3) there lacks a physic explanation of certain parameters of the building performance. Commercial Building Agent-based Model (CoBAM) is a good example of a model used to reconstruct a building stock model using energy consumption survey data (Zhao, Martinez-Moyano and Augenbroe, 2011;Zhao, Lee and Augenbroe, 2015). A data-driven machine learning model that integrates physics-based energy simulation is proposed for multiple scales from a single building to the urban level (Nutkiewicz, Yang and Jain, 2017).…”

Section: Modeling Approachesmentioning

confidence: 99%

Ten questions on urban building energy modeling

Hong

Chen

Luo

et al. 2020

Building and Environment

Self Cite

294

121

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Buildings in cities consume up to 70% of all primary energy. To achieve cities' energy and climate goals, it is necessary to reduce energy use and associated greenhouse gas emissions in buildings through energy conservation and efficiency improvements. Computational tools empowered with rich urban datasets can model performance of buildings at the urban scale to provide quantitative insights for stakeholders and inform their decision making on urban energy planning, as well as building energy retrofits at scale, to achieve efficiency, sustainability, and resilience of urban buildings. Designing and operating urban buildings as a group (from a city block to a district to an entire city) rather than as single individuals requires simulation and optimization to account for interactions among buildings and between buildings and their surrounding urban environment, and for district energy systems serving multiple buildings with diverse thermal loads across space and time. When hundreds or more buildings are involved in typical urban building energy modeling (UBEM) to estimate annual energy demand, evaluate design or retrofit options, and quantify impacts of extreme weather events or climate change, it is crucial to integrate urban datasets and UBEM tools in a seamless automatic workflow with cloud or high-performance computing for users including urban planners, designers and researchers. This paper presents ten questions that highlight significant UBEM research and applications. The proposed answers aim to stimulate discussion and provide insights into the current and future research on UBEM, and more importantly, to inspire new and important questions from young researchers in the field.

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Reconstructing building stock to replicate energy consumption data

Cited by 42 publications

References 11 publications

Global sensitivity analysis as a support for the generation of simplified building stock energy models

Global sensitivity analysis as a support for the generation of simplified building stock energy models

Review on stochastic modeling methods for building stock energy prediction

Ten questions on urban building energy modeling

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