Top-down spatially-explicit probabilistic estimation of building energy performance at a scale

Zhuravchak, Ruslan; Pedrero, Raquel Alonso; Granado, Pedro Crespo del; Nord, Nataša; Brattebø, Helge

doi:10.1016/j.enbuild.2021.110786

Cited by 16 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To support sustainable city or community planning, many studies have put great efforts into urban building energy modeling (UBEM), which can be classified into top-down and bottom-up approaches [8,18]. Top-down methods analyze the long-term relationships between building energy use with society socio-econometric and technological indicators [19,20], e.g., gross domestic product, income, and technological progress [21]. which are all aggregated data without precise spatial or temporal information.…”

Section: Literture Reviewmentioning

confidence: 99%

Times series forecasting for urban building energy consumption based on graph convolutional network

Cheng

Wang

et al. 2022

Applied Energy

View full text Add to dashboard Cite

Section: Literture Reviewmentioning

confidence: 99%

Times series forecasting for urban building energy consumption based on graph convolutional network

Cheng

Wang

et al. 2022

Applied Energy

View full text Add to dashboard Cite

“…The synthesis of this knowledge with the empirical findings made with inverse methods, e.g. exemplified by Zhuravchak et al (2021) may be of significant further interest for large-scale building energy research.…”

Section: Lessons Learnedmentioning

confidence: 99%

Challenges and Lessons Learned in Applying Sensitivity Analysis to Building Stock Energy Models

Fennell¹,

Hove²,

Weinberg³

et al. 2021

Building Simulation Conference Proceedings

View full text Add to dashboard Cite

Uncertainty Analysis (UA) and Sensitivity Analysis (SA) offer essential tools to determine the limits of inference of a model and explore the factors which have the most effect on the model outputs. However, despite a wellestablished body of work applying UA and SA to models of individual buildings, a review of the literature relating to energy models for larger groups of buildings undertaken by Fennell et al. ( 2019) highlighted very limited application at larger scales. This contribution describes the efforts undertaken by a group of research teams in the context of IEA-EBC Annex 70 working with a diverse set of Building Stock Models (BSMs) to apply global sensitivity analysis methods and compare their results. Since BSMs are a class of model defined by their output and coverage rather than their structure and inputs, they represent a diverse set of modelling approaches. Key challenges for the application of SA are identified and explored, including the influence of model form, input data types and model outputs. This study combines results from 7 different modelling teams, each using different models across a range of urban areas to explore these challenges and begin the process of developing standardised workflows for SA of BSMs.

show abstract

“…However, their use is limited in time because based on historical data. They are less prone to provide detailed insights due to their macroscopic nature, but in conjunction with GIS data, a good spacial resolution can be reached as shown in [18]. Conversely, bottom-up approaches try to start from detailed models to describe the stock and compute energy consumption by aggregation.…”

Section: Name Contents Referencesmentioning

confidence: 99%

Bayesian Inference of Dwellings Energy Signature at National Scale: Case of the French Residential Stock

et al. 2021

View full text Add to dashboard Cite

Cities take a central place in today’s energy landscape. Urban Buildings Energy Modeling (UBEM) is identified as a promising approach for energy planning and optimization in cities and districts. It generally relies on the use of Building Archetypes, i.e., simplified deterministic models for categorized building typologies. However, this implies large assumptions which may accumulate and induce significant bias on energy consumption estimates. In this work, we address this issue with static stochastic models whose parameters are inferred over national thermo-energy data using Bayesian Inference. We analyze inference results and validate them with a panel of standard indicators. Then, we provide comparative results with deterministic building archetypes and stock data from the TABULA European project. Comparisons between heat loss coefficients show relative coherence between building categories, but highlight some significant bias between both approaches. This bias is also shown in the comparative result of a Monte Carlo simulation using inferred stochastic models for a 10331 dwellings stock. In conclusion, inferred stochastic models show interesting insights over the French dwellings stock and potential for district energy simulation. All code and data involved in this study are released in an open repository.

show abstract

Top-down spatially-explicit probabilistic estimation of building energy performance at a scale

Cited by 16 publications

References 39 publications

Times series forecasting for urban building energy consumption based on graph convolutional network

Times series forecasting for urban building energy consumption based on graph convolutional network

Challenges and Lessons Learned in Applying Sensitivity Analysis to Building Stock Energy Models

Bayesian Inference of Dwellings Energy Signature at National Scale: Case of the French Residential Stock

Contact Info

Product

Resources

About