Energy efficiency of residential buildings using thermal insulation of external walls and roof based on simulation analysis DOI Creative Commons
Nima Amani

Energy Storage and Saving, Год журнала: 2024, Номер unknown

Опубликована: Дек. 1, 2024

Язык: Английский

Thermal and optical investigations of various transparent wall configurations and building integrated photovoltaic for energy savings in buildings DOI Open Access
Youssef A. Marei, Mohamed Emam,

Mohamed E.A.E. Ahmed

и другие.

Energy Conversion and Management, Год журнала: 2023, Номер 299, С. 117817 - 117817

Опубликована: Ноя. 4, 2023

Язык: Английский

Процитировано

29
A multi-objective optimization framework for performance-based building design considering the interplay between buildings and urban environments DOI

Zhaoyang Qiu,

Qiaoqiao Yong,

Jiayuan Wang

и другие.

Energy Conversion and Management, Год журнала: 2024, Номер 315, С. 118793 - 118793

Опубликована: Июль 15, 2024

Язык: Английский

Процитировано

9
Phase change materials (PCM) as a passive system in the opaque building envelope: A simulation-based analysis DOI Creative Commons

Mojtaba Mousazadeh Aghoei,

Atieh Astanbous,

Reza Yeganeh Khaksar

и другие.

Journal of Energy Storage, Год журнала: 2024, Номер 101, С. 113625 - 113625

Опубликована: Сен. 18, 2024

Язык: Английский

Процитировано

9
Challenges of Artificial Intelligence Development in the Context of Energy Consumption and Impact on Climate Change DOI Creative Commons
Serhii Pimenov, Olena Pimenowa, Piotr Prus

и другие.

Energies, Год журнала: 2024, Номер 17(23), С. 5965 - 5965

Опубликована: Ноя. 27, 2024

With accelerating climate change and rising global energy consumption, the application of artificial intelligence (AI) machine learning (ML) has emerged as a crucial tool for enhancing efficiency mitigating impacts change. However, their implementation dual character: on one hand, AI facilitates sustainable solutions, including optimization, renewable integration carbon reduction; other training operation large language models (LLMs) entail significant potentially undermining neutrality efforts. Key findings include an analysis 237 scientific publications from 2010 to 2024, which highlights advancements obstacles adoption across sectors, such construction, transportation, industry, households. The review showed that interest in use ML grown significantly: over 60% documents have been published last two years, with topics construction forecasting attracting most interest. Most articles are by researchers China, India, UK USA, (28–33 articles). This is more than twice number around rest world; 58% research concentrated three areas: engineering, computer science energy. In conclusion, also identifies areas further aimed at minimizing negative maximizing its contribution development, development energy-efficient architectures new methods management.

Язык: Английский

Процитировано

9
Recent Advances in Machine Learning for Building Envelopes: From Prediction to Optimization DOI
LI Xue-ren, Liwei Zhang, Yin Tang

и другие.

Опубликована: Янв. 1, 2025

Nowadays, advanced building envelopes not only need to meet traditional design requirements but also address emerging demands, such as achieving low-carbon transition of buildings and mitigating the urban heat island (UHI) effect. Given intricacy indoor conditions complexity variables, approaches can hardly keep pace with evolving demands. Therefore, integrating Artificial Intelligence (AI) into envelope is trending in recent years. This paper provides a holistic review research on machine learning (ML) design. Popular ML algorithms, data input requirements, output generation are first elucidated, aiming shed light selection appropriate algorithms for specific datasets achieve optimal outcomes. ML-involved studies related types (e.g., building-integrated photovoltaic (BIPV), green roofs, PCM-integrated walls, glazing systems, etc.) discussed. The further highlights capabilities AI technologies predicting parameters material properties, environmental impact) optimizing criteria minimizing energy consumption), from micro-scope (i.e., microenvironment) macro-scope impact heat). work anticipated yield valuable insights promoting AI-driven solutions tackle both conventional challenges sustainable development.

Язык: Английский

Процитировано

1
Synergy of Human-Centered AI and Cyber-Physical-Social Systems for Enhanced Cognitive Situation Awareness: Applications, Challenges and Opportunities DOI
Saeed Hamood Alsamhi, Santosh Kumar, Ammar Hawbani

и другие.

Cognitive Computation, Год журнала: 2024, Номер 16(5), С. 2735 - 2755

Опубликована: Апрель 11, 2024

Язык: Английский

Процитировано

7
RHTC/HO-BNNS structure collaboratively improves thermal conductivity and photothermal conversion properties of phase change materials DOI

Huizhi Yang,

Mahui Si,

Yujiao guo

и другие.

Solar Energy Materials and Solar Cells, Год журнала: 2024, Номер 268, С. 112746 - 112746

Опубликована: Фев. 8, 2024

Язык: Английский

Процитировано

5
Deep learning–based urban energy forecasting model for residential building energy efficiency DOI Creative Commons
Uma Rani,

Neeraj Dahiya,

Shakti Kundu

и другие.

Energy Exploration & Exploitation, Год журнала: 2024, Номер 42(5), С. 1799 - 1828

Опубликована: Май 30, 2024

Sustainable and inventive city design is becoming more dependent on the use of cutting-edge technology as smart cities develop further. Energy efficiency optimization in residential structures an essential part puzzle it helps conserve resources keeps planet habitable. An enhanced Deep Neural Network (DNN) model for household energy predictions presented this research. Our uses a large dataset building features, weather, occupancy patterns usage histories. Data preprocessed, features are engineered hyperparameters tweaked to improve DNN prediction. Scalable, easy-to-understand models essential, shifting urban areas landscapes. In work, authors have evaluated proposed with basic different optimizers. Initially, Stochastic Gradient Descent optimizer applied that gained 91.02% Recall, 93.47% Precision, 93.28% F1-Score, 0.0153 MSE, 0.0166 RMSE 0.0165 MAE. The 99.52% 98.91% 99.09% 0.0140 0.0137 0.0139 By monitoring, analyzing making decisions real time, systems can help planners understand trends. optimized advances development by promoting sustainability resource optimization. Predicting buildings’ provides proactive savings, cost reduction environmental impact mitigation. suggested shows how planning become sustainable, efficient resilient.

Язык: Английский

Процитировано

4
Eco-efficient green building design: integrating ecosystem-technology for energy efficiency and low-carbon in a cold and dry climate DOI

N F M K Amani

Management of Environmental Quality An International Journal, Год журнала: 2025, Номер unknown

Опубликована: Янв. 8, 2025

Purpose The purpose of this research is to design a residential green building using sustainable approach from an ecosystem-technology perspective in the Darakeh area north Tehran. Design/methodology/approach First step: based on climatic and geographical data, study defined preliminary developed. Second architecture principles comfort zone requirements are analyzed inform process. Third Building modeling energy simulation conducted DesignBuilder software, incorporating technologies. Final building’s performance environmental impact assessed. Findings results show that amount annual production electricity due installation solar collectors roof 12,236 kWh. Considering total 463 m 2 its consumption 17,676 kWh, per square meter surface year 38 Originality/value obtained findings showed designed complies with criteria development building. A Giovanni-based EchoTech (ecosystem-technology) was used research, which greatly increases reliability results.

Язык: Английский

Процитировано

0
A Comprehensive Structural Design-Based Protocol for Energy-Efficient Buildings for Smart City Projects DOI
Arul Jayaraman, M. Vasudevan,

C. Sasikumar

и другие.

Advances in Science, Technology & Innovation/Advances in science, technology & innovation, Год журнала: 2025, Номер unknown, С. 27 - 35

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0