Investigating the reliability of building energy models: Comparative analysis of the impact of data pipelines and model complexities

Saad, Mostafa M.; Eicker, Ursula

doi:10.1016/j.jobe.2023.106511

Cited by 7 publications

(2 citation statements)

References 43 publications

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“…In addition, standardizing the output features by a geometrical unit was performed to allow future comparative analyses. Saad and Eicker compared numerous standardization methods reporting the per conditioned volume area as the least biased within different data types and spatial scales [70]. The study's feature selection processes have resulted in the use of the features in Table 7.…”

Section: Mvlr Resultsmentioning

confidence: 99%

Supporting Decision Making for Building Decarbonization: Developing Surrogate Models for Multi-Criteria Building Retrofitting Analysis

Saad,

Menon,

Eicker

2023

Energies

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Decarbonizing buildings is crucial in addressing pressing climate change issues. Buildings significantly contribute to global greenhouse gas emissions, and reducing their carbon footprint is essential to achieving sustainable and low-carbon cities. Retrofitting buildings to become more energy efficient constitutes a solution. However, building energy retrofits are complex processes that require a significant number of simulations to investigate the possible options, which limits comprehensive investigations that become infeasible to carry out. Surrogate models can be vital in addressing computational inefficiencies by emulating physics-based models and predicting building performance. However, there is a limited focus on investigating feature engineering and selection methods and their effect on the model’s performance and optimization. Feature selection methods are considered effective with interpretable models such as multi-variate linear regression (MVLR) and multiple adaptive regression splines (MARS) for achieving stable prediction stability. This study proposes a modelling framework to create, optimize, and improve the performance of surrogate predictive models for energy consumption, carbon emissions, and the associated cost of building energy retrofit processes. The investigated feature selection methods are wrapper and embedded methods such as backward-stepwise feature selection (BSFS), recursive feature elimination (RFE), and Elastic Net embedded regularization in order to provide insights into the model’s behavior and optimize the model’s performance. The most accurate surrogate models developed achieved a mean absolute percentage error (MAPE) of 0.2–1.8% compared to the used test data. In addition, when calculated for a million samples, all developed surrogate models reduced the computational time by one-thousand-fold compared to physics-based models. The study’s findings pave the way towards low-computational accurate models that can comprehensively predict building performance in near real-time, ultimately leading to identifying decarbonization measures at scale.

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Section: Mvlr Resultsmentioning

confidence: 99%

Supporting Decision Making for Building Decarbonization: Developing Surrogate Models for Multi-Criteria Building Retrofitting Analysis

Saad,

Menon,

Eicker

2023

Energies

Self Cite

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“…Policymakers commonly use geospatial tools to perform these types of analyses. However, a gap exists in the linkage process among the encoding of geospatial datasets and their processing towards creating urban building energy models, which results in discrepancies in accuracy (Saad and Eicker, 2023). For example, the building rating systems that are developed to help quantify the energy usage of individual buildings and, subsequently, their emissions as part of regional and national strategies to reduce energy usage are not often integrated or interoperable with geospatial datasets and tools.…”

Section: Introductionmentioning

confidence: 99%

Enabling Interoperability of Urban Building Energy Data Based on Ogc Api Standards and Citygml 3d City Models

Santhanavanich,

Padsala,

Rossknecht

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This paper presents an investigation into the interoperability of 3D building energy data management, delivery, processing, and visualization via web clients using Open Geospatial Consortium – Application Programming Interface (OGC API) standard-based data models and web interfaces. Specifically, the OGC API – 3D GeoVolumes enable access to 3D city model geometries and semantics on the web, the OGC API – Features support the 2D version of the same geospatial data, the OGC API – Processes are used for CityGML analytics and building energy computation with the SimStadt urban simulation software and the OGC SensorThings API is utilized to manage related spatiotemporal or time-series datasets. The efficacy of this approach has been demonstrated in the OGC Testbed 18 Innovation Program, which highlighted the capacity of OGC API web services to synchronize building energy data and computation results between client and server for the case study of Helsinki, Finland, and Montreal, Canada. The advantages of using OGC API services for 3D building energy data interoperability are discussed, and it is suggested that the use of OGC API be promoted to the general public as well as extended to other domains and on a larger scale in future research.

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Rule-Based System for Intelligent Energy Management in Buildings

Jozi,

Pinto,

Gomes

et al. 2023

Lecture Notes in Computer Science

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Investigating the reliability of building energy models: Comparative analysis of the impact of data pipelines and model complexities

Cited by 7 publications

References 43 publications

Supporting Decision Making for Building Decarbonization: Developing Surrogate Models for Multi-Criteria Building Retrofitting Analysis

Supporting Decision Making for Building Decarbonization: Developing Surrogate Models for Multi-Criteria Building Retrofitting Analysis

Enabling Interoperability of Urban Building Energy Data Based on Ogc Api Standards and Citygml 3d City Models

Rule-Based System for Intelligent Energy Management in Buildings

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