Urban Energy Models Validation in Data Scarcity Context: Case of the Electricity Consumption in the French Residential Sector

Berthou, Thomas; Duplessis, Bruno; Stabat, Pascal; Rivière, Philippe; Marchio, Dominique

doi:10.26868/25222708.2019.210134

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…For each climate, the commercial building stock was represented by 2000 individual buildings for each sector of activity: offices, trades, hospitals, hotels, and restaurant buildings simulated with Smart-E, the simulation platform of Mines ParisTech [43][44][45][46]. Each simulated building had a specific SC surface, geometry, occupancy, and thermal parameters collected.…”

Section: Service Sectormentioning

confidence: 99%

Space Cooling Market in Europe: Assessment of the Final Energy Consumption for the Year 2016

et al. 2022

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This study investigates Europe’s space cooling energy field. The work aims to assess the European Union (plus the United Kingdom) final energy consumption for space cooling in both the residential and service sectors with 2016 as a baseline. An extensive literature review of datasets and journal papers has been conducted to fill the knowledge gaps of the investigated energy branch. The European space cooling market is mainly dominated by vapour compression (VC) technologies which, in this study, have been grouped as room air conditioners (RACs) and centralized air conditioners (CACs). These technology groups have been investigated, and their installed capacities, energy efficiency levels (seasonal energy efficiency ratio—SEER), equivalent full load hours (EFLHs), and amount of space cooling units installed have been identified as essential parameters to calculate the final energy consumption for space cooling. Overall, the total value of the European final energy consumption for the space cooling sector, including both the residential and service sectors, results in 106 TWh/year.

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Section: Service Sectormentioning

confidence: 99%

Space Cooling Market in Europe: Assessment of the Final Energy Consumption for the Year 2016

et al. 2022

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“…However, while the existing legislative framework is conducive to easier access to energy consumption data, respecting data protection legislation [1], in actual fact, it is difficult to obtain real consumption data at an urban scale for validating UBEMs. Typically, these data are protected and managed directly by supply companies, which makes them difficult to access, even for research purposes [31,32].…”

Section: Introductionmentioning

confidence: 99%

Validating ‘GIS-UBEM’—A Residential Open Data-Driven Urban Building Energy Model

García-López,

Sendra,

Domínguez-Amarillo

2024

Sustainability

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The study of energy consumption in buildings, particularly residential ones, brings with it significant socio-economic and environmental implications, as it accounts for approximately 40% of CO2 emissions, 18% in the case of residential buildings, in Europe. On a number of levels, energy consumption serves as a key parameter in urban sustainability indicators and energy plans. Access to data on energy consumption is crucial for energy planning, management, knowledge generation, and awareness. Urban Building Energy Models (UBEMs), which are emerging tools for simulating energy consumption at neighborhood scale, allow for more efficient intervention and energy rehabilitation planning. However, UBEM validation requires reliable reference data, which are often challenging to obtain at urban scale due to privacy concerns and data accessibility issues. Recent advances, such as automation and open data utilization, are proving promising in addressing these challenges. This study aims to provide a standardized UBEM validation process by presenting a case study that was carried out utilizing open data to develop bottom-up engineering models of residential energy demand at urban scale, with a resolution level of individual buildings, and a subsequent adjustment and validation using reference tools. This study confirms that the validated GIS-UBEM model heating and cooling demands and consumption fall within the confidence bands of ±15% and ±12.5%, i.e., the confidence bands required for the approval of official alternative simulation methods for energy certification. This paves the way for its application in urban-scale studies and practices with a well-established margin of confidence, covering a wide range of building typologies, construction models, and climates comparable to those considered in the validation process. The primary application of this model is to determine the starting point and subsequent evaluation of improvement scenarios at a district scale, examining issues such as massive energy rehabilitation interventions, energy planning, demand analysis, vulnerability studies, etc.

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“…Currently, data scarcity represents a scientific bottleneck in many engineering fields (e.g. in healthcare [7], in energy [8], water and environmental engineering [9,10], etc. ), which makes it difficult to apply the latest Machine Learning (ML) methods.…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Learning for Data Scarcity in a Fatigue Damage Prognostic Problem

Akrim¹,

Gogu²,

Vingerhoeds³

et al. 2023

Preprint

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With the increasing availability of data for Prognostics and Health Management (PHM), Deep Learning (DL) techniques are now the subject of considerable attention for this application, often achieving more accurate Remaining Useful Life (RUL) predictions. However, one of the major challenges for DL techniques resides in the difficulty of obtaining large amounts of labelled data on industrial systems. To overcome this lack of labelled data, an emerging learning technique is considered in our work: Self-Supervised Learning, a sub-category of unsupervised learning approaches. This paper aims to investigate whether pre-training DL models in a self-supervised way on unlabelled sensors data can be useful for RUL estimation with only Few-Shots Learning, i.e. with scarce labelled data. In this research, a fatigue damage prognostics problem is addressed, through the estimation of the RUL of aluminum alloy panels (typical of aerospace structures) subject to fatigue cracks from strain gauge data. Synthetic datasets composed of strain data are used allowing to extensively investigate the influence of the dataset size on the predictive performance. Results show that the self-supervised pre-trained models are able to significantly outperform the non-pre-trained models in downstream RUL prediction task, and with less computational expense, showing promising results in prognostic tasks when only limited labelled data is available.

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Urban Energy Models Validation in Data Scarcity Context: Case of the Electricity Consumption in the French Residential Sector

Cited by 6 publications

References 11 publications

Space Cooling Market in Europe: Assessment of the Final Energy Consumption for the Year 2016

Space Cooling Market in Europe: Assessment of the Final Energy Consumption for the Year 2016

Validating ‘GIS-UBEM’—A Residential Open Data-Driven Urban Building Energy Model

Self-Supervised Learning for Data Scarcity in a Fatigue Damage Prognostic Problem

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