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Quantifying Input Parameters Influence in UBEM Simulation Results: The Window-to-Wall Ratio Case
Abstract
In the field of urban building energy models (UBEM), the significant mismatch between simulation results and energy metered data is one of the most important barriers when using them as a planning tool. Their implementation requires a great amount of detailed building-related data, which is not always available. These data could be collected or measured for a small group of existing buildings, however such detailed data collection effort becomes impractical for large urban areas. Therefore, UBEM require certain simplifications and assumptions which can distance the input data from reality, leading to a performance gap. In this sense, knowing the influence of all the input parameters on UBEM simulation results would allow to focus the data collection efforts on the inputs with the greatest sensitivity. In this context, the aim of this research is to quantify the influence of the window-to-wall ratio (WWR) input parameter in an UBEM implemented on the residential building stock in Escaldes-Engordany (Andorra). After gathering the WWR data from a significant sample of the building stock, heating simulations of the entire building stock were carried out using the extreme WWR values, minimum and maximum, in order to analyse the variation of the results. The results obtained show an average difference of 12% between the two heating consumption simulation results. Furthermore, it can also be seen that the differences are more significant for the single-family building typology, as well as for more recently constructed buildings.
... The developed models simulate the thermal performance of buildings at a large scale providing a wide range of performance indicators that enable characterizing the energy use and emission of the building sector [2]. However, given the constraints posed by the sparse data of individual building systems and schedules, coupled with the computational demands of processing such detailed information, the developed models often adopt a simplified approach to represent building inputs, especially occupant-related inputs [3,4]. A recent investigation showed that the vast majority of the currently available UBEM tools can consider differences in geometry, construction, and system characteristics, however, they mostly rely on deterministic schedules with simplified granularity to describe occupancy schedules and occupant behaviours [5]. ...
... Step (4) to generate the urban occupancy profiles was normalizing the generated profiles for each POI based on the maximum number of occupants after removing any outliers. Accordingly, the generated profiles present a ratio between zero and one where zero shows unoccupied hours and one is the peak occupancy of the POI. ...
In the context of electrification for different sectors, demand-side management (DSM) strategies are acknowledged as primary strategies to ensure the stability and reliability of the utility grid. Urban building energy modelling (UBEM) emerges as a critical tool for utilities to assess the impact of these strategies on the building sector's energy consumption and flexibility. However, relying on the developed models for this task is applicable only when detailed occupant-related inputs are integrated into the model. To this end, this paper aims to develop a framework to integrate high-resolution occupancy schedules into UBEM and showcase the application of the developed models in evaluating DSM strategies with different scenarios. The developed framework is applied to a mixed-use district in Montreal, Canada with 112 buildings as a case study. The main objectives of this study are 1) developing an urban scale high-resolution occupancy profile generator representative of Canadian commercial buildings using mobile positioning data, 2) investigating the diversity between the generated profiles of buildings within the same type, 3) developing a method to integrate the generated profiles into the Canadian commercial archetypes, and 4) evaluating the applicability of the developed model in evaluating DSM strategies by investigating the effect of occupant-centric control and occupancy-based demand response strategies on the modelled district energy use. The results of this study serve as a preliminary investigation into the crucial role that occupancy patterns can play in maximizing building energy flexibility with an estimated reduction in district peak demand by up to 17%. This study also paves the way for future research incorporating occupant feedback and comfort requirements for a more precise exploration of the proposed strategy.
The growing demand for energy in urban areas has led to the development of a variety of methodologies for modelling energy in buildings at large scale. However, their accuracy has yet to be thoroughly reviewed. This paper presents a systematic analysis of urban building energy models, that have been validated against measured data, using a singular taxonomy based on key attributes that could influence a model’s accuracy: application, scale, input data, computational method, calibration and validation methods. The analysis showed that the accuracy of urban building energy models is multi-dimensional, considered at a variety of temporal resolutions, spatial resolutions and measures of error, with the results demonstrating that there is no single key attribute that governs it. At the aggregate spatial and annual temporal resolutions, the accuracy, often reported in a single percent error value, can be as low as 1%, while for individual buildings at the annual resolution, the tails of the distribution of errors can reach 1000%. Models using non-calibrated physics-based computational methods were more likely to report overly large errors, while those employing Bayesian calibration consistently reported lower errors at the hourly temporal resolution, demonstrating the positive impact of calibration and in particular the Bayesian approach, on the models’ accuracy. Overall, the review has highlighted that more transparent and consistent reporting of accuracy is necessary and further research is essential for improving the evaluation of accuracy in modelling methodologies, if modern challenges are to be met through emerging applications such as energy systems integration and climate resilience.
Over the past decades, detailed individual building energy models (BEM) on the one side and regional and country-level building stock models on the other side have become established modes of analysis for building designers and energy policy makers, respectively. More recently, these two toolsets have begun to merge into hybrid methods that are meant to analyze the energy performance of neighborhoods, i.e. several dozens to thousands of buildings. This paper reviews emerging simulation methods and implementation workflows for such bottom-up urban building energy models (UBEM). Simulation input organization, thermal model generation and execution, as well as result validation, are discussed successively and an outlook for future developments is presented.
Regulations corroborate the importance of retrofitting existing building stocks or constructing new energy-efficient districts. There is, thus, a need for modeling tools to evaluate energy scenarios to better manage and design cities, and numerous methodologies and tools have been developed. Among them, Urban Building Energy Modelling (UBEM) tools allow the energy simulation of buildings at large scales. Choosing an appropriate UBEM tool, balancing the level of complexity, accuracy, usability, and computing needs, remains a challenge for users. The review focuses on the main bottom-up physics-based UBEM tools, comparing them from a user-oriented perspective. Five categories are used: (i) the required inputs, (ii) the reported outputs, (iii) the exploited workflow, (iv) the applicability of each tool, and (v) the potential users. Moreover, a critical discussion is proposed, focusing on interests and trends in research and development. The results highlighted major differences between UBEM tools that must be considered to choose the proper one for an application. Barriers of adoption of UBEM tools include the needs of a standardized ontology, a common three-dimensional city model, a standard procedure to collect data, and a standard set of test cases. This feeds into future development of UBEM tools to support cities’ sustainability goals.
During recent years, urban building energy modeling has become known as a novel approach for identification, support and improvement of sustainable urban development initiatives and energy efficiency measures in cities. Urban building energy models draw the required information from the energy analysis of buildings in the urban context and suggest options for effective implementation of interventions. The growing interest in urban building energy models among researchers, urban designers and authorities has led to the development of a diversity of models and tools, evolving from physical to more advanced hybrid models. By critically analyzing the published research, this paper incorporates an updated overview of the field of urban building energy modeling and investigates possibilities, challenges and shortcomings, as well as an outlook for future improvements. The survey of previous studies identifies technical bottlenecks and legal barriers in access to data, systematic and inherent uncertainties as well as insufficient resources as the main obstacles. Furthermore, this study suggests that the main route to further improvements in urban building energy modeling is its integration with other urban models, such as climate and outdoor comfort models, energy system models and, in particular, mobility models.
City governments and energy utilities are increasingly focusing on the development of energy efficiency strategies for buildings as a key component in emission reduction plans and energy supply strategies. To support these diverse needs, a new generation of Urban Building Energy Models (UBEM) is currently being developed and validated to estimate citywide hourly energy demands at the building level. However, in order for cities to rely on UBEMs, effective model generation and maintenance workflows are needed based on existing urban data structures.
Reference buildings are useful tools for evaluating energy saving measures of an entire building stock. The objective of this study is to develop a method for obtaining reference buildings for the low-income housing stock in Florianópolis, Southern Brazil. Field data collection was performed in order to build a database on geometrical features of houses. Reference buildings were obtained using cluster analysis, which aims to find subgroups within a sample. For that, hierarchical and non-hierarchical techniques were applied in combination. Reference buildings were taken as the nearest houses to the mean of their clusters. Computer simulation using Energyplus were performed to verify the accuracy of the found reference buildings. The 120 housing units sample database obtained from the survey resulted into two reference buildings after cluster analysis: a 76 m2 house, with living room, kitchen and three bedrooms, and a 37 m2 house, with combined living room and kitchen and two bedrooms. Simulations have shown that the reference buildings can represent their cluster properly, since the degree-hour values obtained for them were similar to the housing median sample. Cluster analysis proved to be a technique useful for obtaining reference buildings, although one should be very careful in selecting the variables involved in the analysis.
There often is a significant difference between predicted (computed) energy performance of buildings and actual measured energy use once buildings are operational. This article reviews literature on this ‘performance gap’. It discerns three main types of gap: (1) between first-principle predictions and measurements, (2) between machine learning and measurements, and (3) between predictions and display certificates in legislation. It presents a pilot study that attempts an initial probabilistic probe into the performance gap. Findings from this pilot study are used to identify a number of key issues that need to be addressed within future investigations of the performance gap in general, especially the fact that the performance gap is a function of time and external conditions. The paper concludes that the performance gap can only be bridged by a broad, coordinated approach that combines model validation and verification, improved data collection for predictions, better forecasting, and change of industry practice.
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