Understanding building energy efficiency with administrative and emerging urban big data by deep learning in Glasgow

Sun, Maoran; Han, Changyu; Nie, Quan; Xu, Jingying; Zhang, Fan; Zhao, Qunshan

doi:10.1016/j.enbuild.2022.112331

Cited by 30 publications

(15 citation statements)

References 44 publications

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“…Review results show that energy flexibility is vital for keeping a power grid sustainable and resilient and a significant measure to decrease utility costs for building owners (Li & Hong, 2023). Moreover, we receive information for building characteristics (e.g., energy consumption) based on machine learning methods from various authors, such as Pham et al (2020), Streltsov et al (2020), andRosenfelder et al (2021), as well as for energy efficiency inputs based on deep learning-based multi-source data fusion frameworks (Sun et al 2022).…”

Section: Resultsmentioning

confidence: 99%

The Role of the Energy Use in Buildings in Front of Climate Change: Reviewing a System’s Challenging Future

Ekonomou¹,

Menegaki²

2023

Preprint

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Energy keeps the global economy alive, whereas it is extensively exposed to various climate change impacts. In this context, severe business competition (e.g., the building sector) and the unwise use of natural resources and ecosystem services (e.g., fossil fuel energy sources) seem to sharpen the relevant effects of climate change. Indicatively, contemporary issues at the interface of building energy performance and environmental quality levels include consequences from global warming, increasing releases of carbon dioxide to peak electrical loads, power grid and building planning, and energy demand and supply issues. In light of such concerns, the present review paper attempts to disclose the multifaceted and multidisciplinary character of building energy use at the interface of the economy, the environment, and society against climate change. This review highlights energy efficiency concepts, production, distribution, consumption patterns, and relevant technological improvements. Interestingly, the reviewed contributions in relevant literature reveal the need and necessity to alter the energy mix used and relevant energy use issues. These include developments in climate-proof and effective systems regarding climate change impacts and shocks. Practical implications indicate that the sustainable development goals for clean energy and climate action should be followed if we wish to bring a sustainable future closer and faster to our reality.

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Section: Resultsmentioning

confidence: 99%

The Role of the Energy Use in Buildings in Front of Climate Change: Reviewing a System’s Challenging Future

Ekonomou¹,

Menegaki²

2023

Preprint

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“…In the first stage, we initialize ResNet‐18 with pre‐trained weights on ImageNet (Deng et al., 2009). This initialization strategy promotes fast convergence, improves accuracy, and reduces training time (Sun et al., 2022; Xing et al., 2020). Although the pre‐trained model can extract high‐level features from the input images, the model needs to be fine‐tuned for specific prediction tasks using the grid building‐stock dataset that we created.…”

Section: Methods and Datamentioning

confidence: 99%

High‐resolution quantification of building stock using multi‐source remote sensing imagery and deep learning

Bao

Huang

Wang

et al. 2023

J of Industrial Ecology

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In recent decades, urbanization has led to an increase in building material stock. The high‐resolution quantification of building stock is essential to understand the spatial concentration of materials, urban mining potential, and sustainable urban development. Current approaches rely excessively on statistics or survey data, both of which are unavailable for most cities, particularly in underdeveloped areas. This study proposes an end‐to‐end deep‐learning model based on multi‐source remote sensing data, enabling the reliable estimation of building stock. Ground‐detail features extracted from optical remote sensing (ORS) and spatiotemporal features extracted from nighttime light (NTL) data are fused and incorporated into the model to improve accuracy. We also compare the performance of our feature‐fusion model with that of an ORS‐only regression model and traditional NTL regression for Beijing. The proposed model yields the best building‐stock estimation, with a Spearman's rank correlation coefficient of 0.69, weighted root mean square error of 0.58, and total error in the test set below 14%. Using gradient‐weighted class activation mapping, we further investigate the relationship between ORS features and building‐stock estimation. Our model exhibits reliable predictive capability and illustrates the tremendous value of the physical environment for estimating building stock. This research illustrates the significant potential of ORS and deep learning for stock estimation. Large‐scale, long‐term building‐stock investigations could also benefit from the end‐to‐end predictability and the data availability of the model.

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“…Sun et al [24] have presented understanding building energy efficacy by administrative with developing urban big data by DL in Glasgow. This research presented multi-source data fusion system for building energy efficacy estimation based on deep learning.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This may be accomplished by adding aspects of chance or indeterminacy using a random number generator. It is shown in equation (24),…”

Section: ) Randomnessmentioning

confidence: 99%

Exploration of Natural Element Form Optimization Algorithm using Spatial-Temporal Multi-Scale Alignment Graph Neural Network in Architectural Design Based on Morphological Theory

Chen Liu

2024

jes

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The construction industry has experienced important changes in recent years due to advancements in digital, artificial intelligence, and construction technologies, as well as the sector's on-going development and the advancement of science and technology. The creative growth of building industry, creative creation of architectural forms are partially supported technically by sophisticated parametric design apparatuses, the potent computing benefits of computer technology. In this manuscript, Exploration of Natural Element Form Optimization Algorithm using Spatial-Temporal Multi-Scale Alignment Graph Neural Network in Architectural Design Based on Morphological Theory (ENEF-OA-ADMT) is proposed. The STMSA-GNN and the Chaotic Coyote Algorithm (CCA) are two tools used by the proposed ENEF-OA-ADMT approach to improve architectural design based on morphological theory. The ST-MSA GNN's ability to capture intricate interactions and dependencies between several components in both space and time allows it to perform a comprehensive study of the morphological aspects of architectural designs. This graph neural network's integration of spatial and temporal dimensions enables a deeper understanding of how the architectural structural form design changes over time. The CCA optimized the ST-MSA-GNN to enhance the architectural structural form design. The proposed ENEF-OA-ADMT methodology skill fully combines these methodologies, creating a strong framework that allows architects and designers to work together to explore, refine, and create architectural structural design forms. The framework provided serves as a spur for further research, encouraging a more complete integration of technology and environment in the architectural domain. The effectiveness of proposed method is executed in python, evaluated through performance metrics encompassing accuracy, precision, specificity, Recall, computational time, F1 score, population diversification, randomness. Proposed ENEF-OA-ADMT method 34.56%, 28.63% and 21.89% higher accuracy, 34.97%, 32.13% and 21.89% higher precision and 34.68%, 20.84% and 29.76% higher randomness when compared with the existing methods such as Study of Morphological Design of Architecture from Geometric Logic Perspective (SOT-MDA-GLP), learning deep morphological networks by neural architecture search (LD-MN-NAS) and identifying degrees of deprivation from space utilizing deep learning with morphological spatial analysis of deprived urban areas (IDDS-DLMSA-DUA) respectively.

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Understanding building energy efficiency with administrative and emerging urban big data by deep learning in Glasgow

Cited by 30 publications

References 44 publications

The Role of the Energy Use in Buildings in Front of Climate Change: Reviewing a System’s Challenging Future

The Role of the Energy Use in Buildings in Front of Climate Change: Reviewing a System’s Challenging Future

High‐resolution quantification of building stock using multi‐source remote sensing imagery and deep learning

Exploration of Natural Element Form Optimization Algorithm using Spatial-Temporal Multi-Scale Alignment Graph Neural Network in Architectural Design Based on Morphological Theory

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