Future smart cities: requirements, emerging technologies, applications, challenges, and future aspects

Cities, Год журнала: 2022, Номер 129, С. 103794 - 103794

Опубликована: Июнь 18, 2022

Язык: Английский

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Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm

Renewable and Sustainable Energy Reviews, Год журнала: 2022, Номер 160, С. 112128 - 112128

Опубликована: Март 5, 2022

Язык: Английский

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Strategies to save energy in the context of the energy crisis: a review

Environmental Chemistry Letters, Год журнала: 2023, Номер 21(4), С. 2003 - 2039

Опубликована: Март 23, 2023

Abstract New technologies, systems, societal organization and policies for energy saving are urgently needed in the context of accelerated climate change, Ukraine conflict past coronavirus disease 2019 pandemic. For instance, concerns about market policy responses that could lead to new lock-ins, such as investing liquefied natural gas infrastructure using all available fossil fuels compensate Russian supply cuts, may hinder decarbonization efforts. Here we review energy-saving solutions with a focus on actual crisis, green alternatives fuel heating, buildings transportation, artificial intelligence sustainable energy, implications environment society. Green include biomass boilers stoves, hybrid heat pumps, geothermal solar thermal photovoltaics systems into electric boilers, compressed hydrogen. We also detail case studies Germany which is planning 100% renewable switch by 2050 developing storage air China, emphasis technical economic aspects. The global consumption 2020 was 30.01% industry, 26.18% transport, 22.08% residential sectors. 10–40% can be reduced sources, passive design strategies, smart grid analytics, energy-efficient building intelligent monitoring. Electric vehicles offer highest cost-per-kilometer reduction 75% lowest loss 33%, yet battery-related issues, cost, weight challenging. 5–30% saved automated networked vehicles. Artificial shows huge potential improving weather forecasting machine maintenance enabling connectivity across homes, workplaces, transportation. 18.97–42.60% through deep neural networking. In electricity sector, automate power generation, distribution, transmission operations, balance without human intervention, enable lightning-speed trading arbitrage decisions at scale, eliminate need manual adjustments end-users.

Язык: Английский

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Sustainable industrial and operation engineering trends and challenges Toward Industry 4.0: a data driven analysis

Journal of Industrial and Production Engineering, Год журнала: 2021, Номер 38(8), С. 581 - 598

Опубликована: Июль 11, 2021

This study supplies contributions to the existing literature with a state-of-the-art bibliometric review of sustainable industrial and operation engineering as field moves toward Industry 4.0, guidance for future studies practical achievements. Although is being promoted forward sustainability, systematization knowledge that forms firms' manufacturing operations encompasses their wide concepts abundant complementary elements still absent. aims analyze contemporary in 4.0 context. The analysis fuzzy Delphi method are proposed. Resulting total 30 indicators criticized clustered into eight groups, including lean cyber-physical production system, big data-driven smart communications, safety security, artificial intelligence circular economy digital environment, business virtual reality, environmental sustainability.

Язык: Английский

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Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Energy & Fuels, Год журнала: 2022, Номер 36(13), С. 6626 - 6658

Опубликована: Июнь 13, 2022

Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity working fluid has a huge impact on efficiency system. addition small amount high thermal conductivity solid nanoparticles to base improves transfer. Even though large research data is available literature, some results are contradictory. Many influencing factors, as well nonlinearity refutations, make nanofluid highly challenging obstruct its potentially valuable uses. On other hand, data-driven machine learning techniques would be very useful for forecasting thermophysical features rate, identifying most influential assessing efficiencies different primary aim this review study look at applications employed nanofluid-based system, reveal new developments research. A variety modern algorithms studies systems examined, along with their advantages disadvantages. Artificial neural networks-based model prediction using contemporary commercial software simple develop popular. prognostic may further improved by combining marine predator algorithm, genetic swarm intelligence optimization, intelligent optimization approaches. In well-known networks fuzzy- gene-based techniques, newer ensemble such Boosted regression K-means, K-nearest neighbor (KNN), CatBoost, XGBoost gaining due architectures adaptabilities diverse types. regularly used fuzzy-based mostly black-box methods, user having little or no understanding how they function. This reason concern, ethical artificial required.

Язык: Английский

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Methods and applications for Artificial Intelligence, Big Data, Internet of Things, and Blockchain in smart energy management

Energy and AI, Год журнала: 2022, Номер 11, С. 100208 - 100208

Опубликована: Окт. 4, 2022

Information technologies involving artificial Intelligence, big data, Internet of Things devices and blockchain have been developed implemented in many engineering fields worldwide. Existing review articles focus on developments characteristics individual topics the associated deployment energy sector. These technologies, all based communication, information, data analysis, are naturally coherent integrable. This article reviews literature patents four closely related aims to provide a holistic view how they their integrability relation smart management strategies. Artificial intelligence models forecast use load profiles as well schedule resources ensure reliable performance effective utilization resources. Training requires immense volumes data. Utilizing systems mining enables discovery new functions relationships, which determines intelligence. Data also refines information; thus, is trained iteratively with more accurate Smart can be further enhanced through advanced digital like blockchain. An platform containing edge, fog cloud layers helps connect other hardware software systems. Furthermore, an efficiently transmits stores improving access availability stakeholders for mining. Emerging such cryptocurrency facilitate trading designed layer supplement storage. Providing efficient seamless integration intelligence, will important factor emerging transition sector lower-carbon system.

Язык: Английский

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Energetics Systems and artificial intelligence: Applications of industry 4.0

Energy Reports, Год журнала: 2021, Номер 8, С. 334 - 361

Опубликована: Дек. 16, 2021

Industrial development with the growth, strengthening, stability, technical advancement, reliability, selection, and dynamic response of power system is essential. Governments companies invest billions dollars in technologies to convert, harvest, rising demand, changing demand supply patterns, efficiency, lack analytics required for optimal energy planning, store energy. In this scenario, artificial intelligence (AI) starting play a major role market. Recognizing importance AI, study was conducted on seven different energetics systems their variety applications, including: i) electricity production; ii) delivery; iii) electric distribution networks; iv) storage; v) saving, new materials, devices; vi) efficiency nanotechnology; vii) policy, economics. The main drivers are four key techniques used current AI technologies, fuzzy logic systems; neural genetic algorithms; expert systems. developed countries, industry has started using connect smart meters, grids, Internet Things devices. These will lead improvement management, transparency, usage renewable energies. recent decades/years, technology brought significant improvements how devices monitor data, communicate system, analyze input–output, display data unprecedented ways. New applications become feasible when these developments incorporated into industry. But contrary, much more investment needed global research data-driven models. terms supply, can help utilities provide customers affordable from complex sources secure manner, while at same time providing opportunity use own efficiently. Moreover, policy recommendations, opportunities, 4.0 improve sustainability have been briefly described.

Язык: Английский

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Using the internet of things in smart energy systems and networks

Sustainable Cities and Society, Год журнала: 2021, Номер 68, С. 102783 - 102783

Опубликована: Фев. 21, 2021

Язык: Английский

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Assessing the environmental impacts of renewable energy sources: A case study on air pollution and carbon emissions in China

Journal of Environmental Management, Год журнала: 2023, Номер 345, С. 118525 - 118525

Опубликована: Июль 6, 2023

This study investigates the impact of renewable and non-renewable energy sources on carbon emissions in context China's 14th Five-Year Plan (2021-2025). The plan emphasises a "Dual-control" strategy simultaneously setting consumption limits reducing intensity for GDP (gross domestic product) order to meet targets five-year plan. Using comprehensive dataset Chinese macroeconomic information spanning from 1990 2022, we conduct Granger causality analysis explore relationship between level air pollution. Our findings reveal unidirectional link, wherein contributes reduction pollution, while lead an increase. Despite government's investment energy, our results show that economy remains heavily reliant traditional (e.g., fossil fuels). research is first systematic examination interplay usage context. provide valuable insights policy market strategies aimed at promoting neutrality driving technological advancements both government industries.

Язык: Английский

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The perceived relationship between digitalization and ecological, economic, and social sustainability

Journal of Cleaner Production, Год журнала: 2021, Номер 315, С. 128128 - 128128

Опубликована: Июнь 28, 2021

Sustainability, in terms of ecological, economic, and social sustainable development, the advancing digitalization represent some most substantial societal challenges today. However, little is known about how different actors decision-makers perceive relationship those two challenges. In our paper, by building upon framing theory representations theory, we address that gap investigating interrelationship between sustainability. Such research particularly important because understandings sustainability determine actors, including managers policymakers, act response to imperatives. Following a multi-method approach, combined media analysis with experimental studies examining various frame discourses which dimension sustainability—ecological, or social—dominates. Building these results, assess whether extent affects perception three dimensions. Among findings, perceptions ecological economic but not seem be affected digitalization. For future research, findings indicate need for more nuanced view on accounts its dimensions, especially Beyond that, perceived link guides respond imperatives, work also has practical implications as well.

Язык: Английский

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