Internet of things for smart factories in industry 4.0, a review

Internet of Things and Cyber-Physical Systems, Год журнала: 2023, Номер 3, С. 192 - 204

Опубликована: Янв. 1, 2023

The Internet of Things (IoT) is playing a significant role in the transformation traditional factories into smart Industry 4.0 by using network interconnected devices, sensors, and software to monitor optimize production process. Predictive maintenance IoT can also be used prevent machine failures, reduce downtime, extend lifespan equipment. To energy usage during part manufacturing, manufacturers obtain real-time insights consumption patterns deploying sensors factories. Also, provide more comprehensive view factory environment enhance workplace safety identifying potential hazards alerting workers dangers. Suppliers use IoT-enabled tracking devices shipments updates on delivery times locations order analyze supply chain Moreover, powerful technology which inventory management costs, improve efficiency, visibility levels movements. impact internet thing industry 4.0, review presented. Applications things such as predictive maintenance, asset tracking, management, quality control, process monitoring, efficiency optimization are reviewed. Thus, analyzing application new ideas advanced methodologies provided control processes.

Язык: Английский

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Artificial intelligence-based solutions for climate change: a review

Environmental Chemistry Letters, Год журнала: 2023, Номер 21(5), С. 2525 - 2557

Опубликована: Июнь 13, 2023

Abstract Climate change is a major threat already causing system damage to urban and natural systems, inducing global economic losses of over $500 billion. These issues may be partly solved by artificial intelligence because integrates internet resources make prompt suggestions based on accurate climate predictions. Here we review recent research applications in mitigating the adverse effects change, with focus energy efficiency, carbon sequestration storage, weather renewable forecasting, grid management, building design, transportation, precision agriculture, industrial processes, reducing deforestation, resilient cities. We found that enhancing efficiency can significantly contribute impact change. Smart manufacturing reduce consumption, waste, emissions 30–50% and, particular, consumption buildings 30–50%. About 70% gas industry utilizes technologies enhance accuracy reliability forecasts. Combining smart grids optimize power thereby electricity bills 10–20%. Intelligent transportation systems dioxide approximately 60%. Moreover, management design cities through application further promote sustainability.

Язык: Английский

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Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: Challenges and future perspectives

Energy and AI, Год журнала: 2022, Номер 10, С. 100195 - 100195

Опубликована: Авг. 5, 2022

The vigorous expansion of renewable energy as a substitute for fossil is the predominant route action to achieve worldwide carbon neutrality. However, clean supplies in multi-energy building districts are still at preliminary stages paradigm transitions. In particular, technologies and methodologies large-scale integrations not sufficiently sophisticated, terms intelligent control management. Artificial (AI) techniques powered systems can learn from bio-inspired lessons provide power with intelligence. there few in-depth dissections deliberations on roles AI decarbonisation systems. This study summarizes commonly used AI-related approaches discusses their functional advantages when being applied various sectors, well contribution optimizing operational modalities improving overall effectiveness. also presents practical applications integration systems, analyzes effectiveness through theoretical explanations diverse case studies. addition, this introduces limitations challenges associated neutrality transition using relevant techniques, proposes further promising research perspectives recommendations. comprehensive review ignites advanced provides valuable informational instructions guidelines different stakeholders (e.g., engineers, designers scientists) transition.

Язык: Английский

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Bipolar q-ROF Hybrid Decision Making Model With Golden Cut for Analyzing the Levelized Cost of Renewable Energy Alternatives

IEEE Access, Год журнала: 2022, Номер 10, С. 42507 - 42517

Опубликована: Янв. 1, 2022

Energy costs are the key factors regarding selection of appropriate renewable energy (RWG) alternatives. All a power plant, such as investment, operation, maintenance, and repair considered in scope levelized costs. Therefore, for effective determination selling price energy, cost has guiding role. Because RWG alternatives develop sustainable production consumption long term, leading indicators these should be analyzed significantly. Accordingly, this study, it is aimed to investigate by using bipolar q-rung orthopair fuzzy (q-ROF) hybrid decision-making approach. The novelty study recommend an integrated model based on q-ROFSs with golden cut. At first stage, q-ROF multi stepwise weight assessment ratio analysis (M-SWARA) employed weighting selected criteria following technique order preference similarity ideal solution (TOPSIS) rank terms performance. On other side, vise kriterijumska optimizacija i kompromisno resenje (VIKOR) also In addition issue, sensitivity performed four cases comparatively. Hence, consistency, reliability coherency proposed can measured. It identified that capacity loss greatest importance projects. Solar found best clean type respect management context, would investors design projects close center. This will contribute increasing efficiency productivity

Язык: Английский

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Does artificial intelligence promote green innovation? An assessment based on direct, indirect, spillover, and heterogeneity effects

Energy & Environment, Год журнала: 2023, Номер unknown

Опубликована: Дек. 25, 2023

This paper investigates the intricate relationship between artificial intelligence (AI) and green innovation within context of sustainable development goals. As societies strive to achieve sustainability, understanding dynamics technological advancements environmental progress becomes paramount. Drawing from panel data encompassing 51 countries 2000 2019, this study employs fixed-effects models, mediated effects spatial Durbin models meticulously examine influence AI on innovation. The empirical findings reveal a robust significantly positive correlation innovation, highlighting critical role in fostering Heterogeneity analysis across developed developing economies delineates variations impact shedding light economic levels financial structures. Developed nations showcase more pronounced AI-green compared their counterparts, complexities technology adoption distinct landscapes. Moreover, delves into transmission mechanisms underlying nexus, revealing mediating roles industrial structure human capital. Industrial upgrading enhancement capital emerge as crucial pathways through which indirectly stimulates Spatial analyses reveals relevance globally, emphasizing AI's substantial not only domestic spheres but also neighboring regions. There are significant direct, indirect, total its spillover characteristics catalytic it plays driving collaborative global scale. research contributes nuanced insights interplay providing foundation for policymakers, businesses, researchers comprehend multifaceted dimensions interventions emphasize imperative efforts utilizing potential propel thereby advancing sustainability agendas.

Язык: Английский

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Ecological footprints, carbon emissions, and energy transitions: the impact of artificial intelligence (AI)

Humanities and Social Sciences Communications, Год журнала: 2024, Номер 11(1)

Опубликована: Авг. 14, 2024

Abstract This study examines the multifaceted impact of artificial intelligence (AI) on environmental sustainability, specifically targeting ecological footprints, carbon emissions, and energy transitions. Utilizing panel data from 67 countries, we employ System Generalized Method Moments (SYS-GMM) Dynamic Panel Threshold Models (DPTM) to analyze complex interactions between AI development key metrics. The estimated coefficients benchmark model show that significantly reduces footprints emissions while promoting transitions, with most substantial observed in followed by footprint reduction reduction. Nonlinear analysis indicates several insights: (i) a higher proportion industrial sector diminishes inhibitory effect but enhances its positive transitions; (ii) increased trade openness amplifies AI’s ability reduce promote (iii) benefits are more pronounced at levels development, enhancing (iv) as transition process deepens, effectiveness reducing increases, role further transitions decreases. enriches existing literature providing nuanced understanding offers robust scientific foundation for global policymakers develop sustainable management frameworks.

Язык: Английский

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Reliability evaluation of energy storage systems combined with other grid flexibility options: A review

Journal of Energy Storage, Год журнала: 2023, Номер 63, С. 107022 - 107022

Опубликована: Март 9, 2023

Язык: Английский

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Comparing Long Short-Term Memory (LSTM) and bidirectional LSTM deep neural networks for power consumption prediction

Energy Reports, Год журнала: 2023, Номер 10, С. 3315 - 3334

Опубликована: Окт. 11, 2023

Electric consumption prediction methods are investigated for many reasons, such as decision-making related to energy efficiency well anticipating demand and the dynamics of market. The objective present work is compare two Deep Learning models, namely Long Short-Term Memory (LSTM) model, Bi-directional LSTM (BLSTM) univariate electric Time Series (TS) short-term forecast model. Data Sets (DSs) were selected their different contexts scales, with goal assessing robustness models. Four DSs used, power of: (a) a household in France; (b) university building Santarém, Brazil; (c) Tétouan city zones, Morocco; (d) aggregated Singapore. metrics RMSE, MAE, MAPE R2 calculated TS cross-validation scheme. Friedman's test was applied normalized RMSE (NRMSE) results, showing that BLSTM outperforms statistically significant difference (p = 0.0455), corroborating fact bidirectional weight updating significantly improves performance respect scales consumption. provides statistical evidence supporting conclusion models according tests performed, based on complete methodology prediction, also establishes baseline future investigation prediction.

Язык: Английский

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Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review

Energies, Год журнала: 2023, Номер 16(10), С. 4025 - 4025

Опубликована: Май 11, 2023

The use of machine learning and data-driven methods for predictive analysis power systems offers the potential to accurately predict manage behavior these by utilizing large volumes data generated from various sources. These have gained significant attention in recent years due their ability handle amounts make accurate predictions. importance particular momentum with transformation that traditional system underwent as they are morphing into smart grids future. transition towards embed high-renewables electricity is challenging, generation renewable sources intermittent fluctuates weather conditions. This facilitated Internet Energy (IoE) refers integration advanced digital technologies such Things (IoT), blockchain, artificial intelligence (AI) systems. It has been further enhanced digitalization caused COVID-19 pandemic also affected energy sector. Our review paper explores prospects challenges using provides an overview ways which constructing can be applied order them more efficient. begins description role operations. Next, discusses systems, including benefits limitations. In addition, reviews existing literature on this topic highlights used Furthermore, it identifies opportunities associated methods, quality availability, discussed. Finally, concludes a discussion recommendations research application future grid-driven powered IoE.

Язык: Английский

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A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector

Energies, Год журнала: 2023, Номер 16(3), С. 1077 - 1077

Опубликована: Янв. 18, 2023

There is an ongoing, revolutionary transformation occurring across the globe. This altering established processes, disrupting traditional business models and changing how people live their lives. The power sector no exception going through a radical of its own. Renewable energy, distributed energy sources, electric vehicles, advanced metering communication infrastructure, management algorithms, efficiency programs new digital solutions drive change in sector. These changes are fundamentally supply chains, shifting geopolitical powers revising landscapes. Underlying infrastructural components expected to generate enormous amounts data support these applications. Facilitating flow information coming from system′s prerequisite for applying Artificial Intelligence (AI) New components, flows AI techniques will play key role demand forecasting, system optimisation, fault detection, predictive maintenance whole string other areas. In this context, digitalisation becoming one most important factors sector′s process. Digital possess significant potential resolving multiple issues chain. Considering growing importance AI, paper explores current status technology’s adoption rate review conducted by analysing academic literature but also several hundred companies around world that developing implementing on grid’s edge.

Язык: Английский

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