Reducing urban energy consumption and carbon emissions: a novel GIS-based model for sustainable spatial accessibility to local services and resources DOI Creative Commons

Behzad Rahmati,

Hamidreza Rabiei‐Dastjerdi, Simon Elias Bibri

и другие.

Computational Urban Science, Год журнала: 2024, Номер 4(1)

Опубликована: Ноя. 18, 2024

Abstract This study explores the complex interconnections among global population growth, energy consumption, CO 2 production, and disparities in service access through lens of a single case study. Rapid growth many major cities has created significant challenges related to equitable services socio-economic development, thereby impacting both their consumption patterns environmental impacts. The investigated this study, like other cases developing countries, exhibits differences provision, infrastructure usage, particularly between northern southern regions, which significantly affect quality life, sustainability, economic development. Previous efforts narrow these geographic have yielded limited success exhibited several shortcomings. By employing GIS Analytical Network Process method, examines accessibility single-case city, with particular emphasis on green spaces, food services, educational facilities services. GIS-based approach seeks achieve sustainable levels multiple land uses by evaluating identifying areas overlap them. endeavors increase density standards when planning placement new based locations. method developed represents critical stride toward achieving key objectives. findings reveal that only 47% city blocks enjoy high accessibility, while 40% moderate 2.6% experience poor accessibility. These insights are value urban planners, researchers, policymakers striving reduce shortages promote transportation strategies mitigate impact areas.

Язык: Английский

Artificial intelligence and machine learning in production efficiency enhancement and sustainable development: a comprehensive bibliometric review DOI Creative Commons
Aristidis Bitzenis, Νίκος Κουτσουπιάς,

Marios Nosios

и другие.

Frontiers in Sustainability, Год журнала: 2025, Номер 5

Опубликована: Янв. 13, 2025

This research presents a comprehensive bibliometric review of the role Artificial Intelligence (AI) and Machine Learning (ML) in enhancing production efficiency fostering sustainable development. With increasing focus on sustainability, AI ML technologies have emerged as pivotal tools for optimizing industrial processes, improving resource management minimizing environmental impacts. The study analyzes key algorithms various settings. conducts systematic analysis using Scopus database Bibliometrix R package, examining global trends, collaborations, thematic focuses applications Novel contributions include uncovering underexplored ethical dimensions adoption emphasizing SMEs developing economies advancing practices. Key trends identified integration with energy management, circular economy practices, precision agriculture. Furthermore, reveals geographical contributions, countries like China, United States, Kingdom leading output impact. Despite promising advancements, identifies gaps considerations, especially data privacy labor market implications, suggests avenues future research, including implementation Small Medium Enterprises (SMEs).

Язык: Английский

Процитировано

0
Synergistic integration of digital twins and zero energy buildings for climate change mitigation in sustainable smart cities: A systematic review and novel framework DOI Creative Commons

Simon Elias Bibri,

Jeffrey Huang, Osama Omar

и другие.

Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115484 - 115484

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Day-Ahead Energy Price Forecasting with Machine Learning: Role of Endogenous Predictors DOI Creative Commons
Chibuike Chiedozie Ibebuchi

Forecasting, Год журнала: 2025, Номер 7(2), С. 18 - 18

Опубликована: Апрель 9, 2025

Accurate Day-Ahead Energy Price (DAEP) forecasting is essential for optimizing energy market operations. This study introduces a machine learning framework to predict the DAEP with 24 h lead time, leveraging historical data and forecasts available at prediction time. Hourly from California Independent System Operator (January 2017 July 2023) were integrated exogenous engineered endogenous features. A custom rolling window cross-validation, validation blocks sliding daily across 2372 folds, evaluates an Extreme Gradient Boosting (XGBoost) model’s performance under diverse conditions, achieving median mean absolute error of 6.26 USD/MWh root squared 8.27 USD/MWh, variability reflecting volatility. The feature importance analysis using Shapley additive explanations highlighted dominance features in driving time relatively stable conditions. Forecasting runtime 10 AM on prior day was used assess model uncertainty. involved training random forest, support vector regression, XGBoost, feed forward neural network models, followed by stacking voting ensembles. results indicate need ensemble evaluation beyond static train–test split ensure practical utility varied dynamics. Finally, operationalizing forecast bidding decisions real-time prices presented discussed.

Язык: Английский

Процитировано

0
The Integration of Machine Learning and Explainable AI and Business Digitization: Unleashing the Power of Data - A Review DOI
Nipuna Sankalpa Thalpage

Journal of Digital Science, Год журнала: 2024, Номер 6(1), С. 18 - 27

Опубликована: Июнь 27, 2024

The integration of machine learning (ML) and explainable artificial intelligence (XAI) within business digitization is a critical area for innovation enhanced decision-making. This review synthesizes recent literature, sourced from academic databases like IEEE Xplore, Springer, ScienceDirect, PubMed, focusing on peer-reviewed studies the last five years to ensure relevance. Key applications ML across healthcare, finance, marketing are explored, highlighting its ability handle complex datasets improve predictive accuracy. discusses AutoML automating model building, making advanced analytics more accessible, examines synergy between IoT in small medium-sized enterprises (SMEs) efficiency. Explainable AI (XAI)'s role providing transparency, building trust, ensuring ethical deployment also underscored. findings indicate that strategic XAI use enhances operational efficiency decision-making, comprehensive overview current trends, applications, benefits, challenges, future research directions.

Язык: Английский

Процитировано

0
Applications of Machine Learning in Manufacturing, Healthcare, Finance, Agriculture, Retail, Energy, and Transportation: A Review DOI

Nitin Rane,

Suraj Kumar Mallick,

Ömer Kaya

и другие.

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0
Reducing urban energy consumption and carbon emissions: a novel GIS-based model for sustainable spatial accessibility to local services and resources DOI Creative Commons

Behzad Rahmati,

Hamidreza Rabiei‐Dastjerdi, Simon Elias Bibri

и другие.

Computational Urban Science, Год журнала: 2024, Номер 4(1)

Опубликована: Ноя. 18, 2024

Abstract This study explores the complex interconnections among global population growth, energy consumption, CO 2 production, and disparities in service access through lens of a single case study. Rapid growth many major cities has created significant challenges related to equitable services socio-economic development, thereby impacting both their consumption patterns environmental impacts. The investigated this study, like other cases developing countries, exhibits differences provision, infrastructure usage, particularly between northern southern regions, which significantly affect quality life, sustainability, economic development. Previous efforts narrow these geographic have yielded limited success exhibited several shortcomings. By employing GIS Analytical Network Process method, examines accessibility single-case city, with particular emphasis on green spaces, food services, educational facilities services. GIS-based approach seeks achieve sustainable levels multiple land uses by evaluating identifying areas overlap them. endeavors increase density standards when planning placement new based locations. method developed represents critical stride toward achieving key objectives. findings reveal that only 47% city blocks enjoy high accessibility, while 40% moderate 2.6% experience poor accessibility. These insights are value urban planners, researchers, policymakers striving reduce shortages promote transportation strategies mitigate impact areas.

Язык: Английский

Процитировано

0