Localizing urban building energy modeling (UBEM) through inclusive microclimate and socioeconomic data DOI

Tarek Sherif,

Riwayat Katia, M. Nguyen

и другие.

Applied Energy, Год журнала: 2025, Номер 383, С. 125342 - 125342

Опубликована: Янв. 17, 2025

Язык: Английский

Building energy simulation and its application for building performance optimization: A review of methods, tools, and case studies DOI Creative Commons
Yiqun Pan,

Mingya Zhu,

Yan Lv

и другие.

Advances in Applied Energy, Год журнала: 2023, Номер 10, С. 100135 - 100135

Опубликована: Апрель 6, 2023

As one of the most important and advanced technology for carbon-mitigation in building sector, performance simulation (BPS) has played an increasingly role with powerful support energy modelling (BEM) energy-efficient designs, operations, retrofitting buildings. Owing to its deep integration multi-disciplinary approaches, researchers, as well tool developers practitioners, are facing opportunities challenges during application BEM at multiple scales stages, e.g., building/system/community levels planning/design/operation stages. By reviewing recent studies, this paper aims provide a clear picture how performs solving different research questions on varied phase spatial resolution, focus objectives frameworks, methods tools, applicability transferability. To guide future applications performance-driven management, we classified current trends into five topics that span through stages levels: (1) Simulation design new retrofit design, (2) Model-based operational optimization, (3) Integrated using data measurements digital twin, (4) Building supporting urban planning, (5) Modelling building-to-grid interaction demand response. Additionally, recommendations discussed, covering potential occupancy behaviour modelling, machine learning, quantification model uncertainties, linking monitoring systems.

Язык: Английский

Процитировано

153

Data acquisition for urban building energy modeling: A review DOI
Chao Wang, Martina Ferrando, Francesco Causone

и другие.

Building and Environment, Год журнала: 2022, Номер 217, С. 109056 - 109056

Опубликована: Апрель 8, 2022

Язык: Английский

Процитировано

110

AutoBPS: A tool for urban building energy modeling to support energy efficiency improvement at city-scale DOI
Deng Zhang, Yixing Chen, Jingjing Yang

и другие.

Energy and Buildings, Год журнала: 2023, Номер 282, С. 112794 - 112794

Опубликована: Янв. 11, 2023

Язык: Английский

Процитировано

90

Towards intelligent building energy management: AI-based framework for power consumption and generation forecasting DOI
Samee U. Khan, Noman Khan,

Fath U Min Ullah

и другие.

Energy and Buildings, Год журнала: 2022, Номер 279, С. 112705 - 112705

Опубликована: Дек. 5, 2022

Язык: Английский

Процитировано

89

Physically Consistent Neural Networks for building thermal modeling: Theory and analysis DOI Creative Commons
Loris Di Natale, Bratislav Svetozarevic, Philipp Heer

и другие.

Applied Energy, Год журнала: 2022, Номер 325, С. 119806 - 119806

Опубликована: Авг. 19, 2022

Due to their high energy intensity, buildings play a major role in the current worldwide transition. Building models are ubiquitous since they needed at each stage of life buildings, i.e. for design, retrofitting, and control operations. Classical white-box models, based on physical equations, bound follow laws physics but specific design underlying structure might hinder expressiveness hence accuracy. On other hand, black-box better suited capture nonlinear building dynamics thus can often achieve accuracy, require lot data not physics, problem that is particularly common neural network (NN) models. To counter this known generalization issue, physics-informed NNs have recently been introduced, where researchers introduce prior knowledge ground them avoid classical NN issues. In work, we present novel architecture, dubbed Physically Consistent (PCNN), which only requires past operational no engineering overhead, including linear module running parallel NN. We formally prove such networks physically consistent – by even unseen with respect different inputs temperatures outside neighboring zones. demonstrate performance case study, PCNN attains an accuracy up 40% than physics-based resistance-capacitance model 3-day long prediction horizons. Furthermore, despite constrained structure, PCNNs attain similar validation data, overfitting training less retaining tackle issue.

Язык: Английский

Процитировано

80

Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach DOI Creative Commons
Usman Ali,

Sobia Bano,

Mohammad Haris Shamsi

и другие.

Energy and Buildings, Год журнала: 2023, Номер 303, С. 113768 - 113768

Опубликована: Ноя. 22, 2023

Stakeholders such as urban planners and energy policymakers use building performance modeling analysis to develop strategic sustainable plans with the aim of reducing consumption emissions from built environment. However, inconsistent data lack scalable models create a gap between traditional planning practices. An alternative approach is conduct large-scale usage survey, which time-consuming. Similarly, existing studies rely on machine learning or statistical approaches for calculating performance. This paper proposes solution that employs data-driven predict residential buildings, using both ensemble-based end-use demand segregation methods. The proposed methodology consists five steps: collection, archetype development, physics-based parametric modeling, analysis. devised tested Irish stock generates synthetic dataset one million buildings through 19 identified vital variables four archetypes. As part process, study implemented an method, including heating, lighting, equipment, photovoltaic, hot water, at scale. Furthermore, model's enhanced by employing approach, achieving 91% accuracy compared approach's 76%. Accurate prediction enables stakeholders, planners, make informed decisions when retrofit measures.

Язык: Английский

Процитировано

54

Predicting solar radiation in the urban area: A data-driven analysis for sustainable city planning using artificial neural networking DOI
Alireza Attarhay Tehrani, Omid Veisi, Bahereh Vojdani Fakhr

и другие.

Sustainable Cities and Society, Год журнала: 2023, Номер 100, С. 105042 - 105042

Опубликована: Ноя. 17, 2023

Язык: Английский

Процитировано

51

A new approach for national-scale Building Energy Models based on Energy Performance Certificates in European countries: The case of Spain DOI Creative Commons
Carlos Beltrán-Velamazán, Marta Monzón-Chavarrías, Belinda López-Mesa

и другие.

Heliyon, Год журнала: 2024, Номер 10(3), С. e25473 - e25473

Опубликована: Фев. 1, 2024

Urban Building Energy Models (UBEMs) are useful instruments to know the energy consumption of building stocks at urban and national levels. UBEMs can be classified into different types subtypes. The current detailed physics-based bottom-up a scale play crucial role in assessing efficiency defining improvement strategies. These models heavily rely on archetypes simulations, demanding significant computational resources. We propose here new type national-scale UBEM based Performance Certificates (EPCs), other open big data, which has advantage that it automatically updated, short time, with standard computer means. In this paper, we define methodology build EPC-based UBEM. have checked model for case Spain generated updated less than 6 h computer, generates results match official data more 98 % four indicators. contains information about 10,939,801 buildings Spain, out 1,202,708 EPCs. allows us map analyse country by integrating multiple variables nature, such as geographical (Autonomous Community, municipality, municipality), physical (area, number floors, date construction), use-related (main use each its units), energy-related (climate zone, class, consumption, CO2 emissions). proven development some indicators measure progress decarbonisation trajectories whose will become mandatory European Member States soon.

Язык: Английский

Процитировано

20

A roadmap for the implementation of a renewable energy community DOI Creative Commons
Paolo Esposito, Elisa Marrasso, Chiara Martone

и другие.

Heliyon, Год журнала: 2024, Номер 10(7), С. e28269 - e28269

Опубликована: Март 22, 2024

Environmental problems due to climate change, that have been affecting our planet for years, are the main issues which prompted European Union establish ambitious target of achieving carbon neutrality by 2050. This occurrence encouraged all Member States undergo significant changes their energy sectors, favouring extensive use renewable sources. In this scenario, introduced Renewable Energy Communities, innovative systems based on a new model production, consumption and sharing, guaranteeing environmental, economic, social benefits. The objective paper is twofold: firstly, examine regulatory framework and, secondly, present standardized procedure implementation Community, an aspect not yet covered in scientific literature. roadmap includes four phases: feasibility study involving analysis end users' general assessment; aggregation members as producers, consumers or prosumers forming legal entity, considering different funding opportunities; operating phase, plant construction project validation national authorities; technical economic management phase. dynamic structure allows adjustments accommodate contexts, member typologies aim.

Язык: Английский

Процитировано

20

From building energy modeling to urban building energy modeling: A review of recent research trend and simulation tools DOI
Graziano Salvalai, Yunxi Zhu, Marta Maria Sesana

и другие.

Energy and Buildings, Год журнала: 2024, Номер 319, С. 114500 - 114500

Опубликована: Июль 3, 2024

Язык: Английский

Процитировано

18