Explainable Artificial Intelligence (XAI)

Advances in environmental engineering and green technologies book series, Год журнала: 2024, Номер unknown, С. 333 - 362

Опубликована: Окт. 16, 2024

Explainable AI (XAI) is important in situations where decisions have significant effects on the results to make systems more reliable, transparent, and people understand how work. In this chapter, an overview of AI, its evolution are discussed, emphasizing need for robust policy regulatory frameworks responsible deployment. Then key concept use XAI models been discussed. This work highlights XAI's significance sectors like healthcare, finance, transportation, retail, supply chain management, robotics, manufacturing, legal criminal justice, etc. profound human societal impacts. Then, with integrated IoT renewable energy management scope smart cities addressed. The study particularly focuses implementations solutions, specifically solar power integration, addressing challenges ensuring transparency, accountability, fairness AI-driven decisions.

Язык: Английский

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Major challenges for commercialization of perovskite solar cells: A critical review

Energy Reports, Год журнала: 2025, Номер 13, С. 1400 - 1415

Опубликована: Янв. 16, 2025

Язык: Английский

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Hybrid renewable multi-generation system optimization: Attaining sustainable development goals

Renewable and Sustainable Energy Reviews, Год журнала: 2025, Номер 212, С. 115415 - 115415

Опубликована: Янв. 24, 2025

Язык: Английский

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Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

International Journal of Renewable Energy Development, Год журнала: 2024, Номер 13(2)

Опубликована: Фев. 10, 2024

The escalating trends in energy consumption and the associated emissions of pollutants past century have led to depletion environmental pollution. Achieving comprehensive sustainability requires optimization efficiency implementation efficient management strategies. Artificial intelligence (AI), a prominent machine learning paradigm, has gained significant traction control applications found extensive utility various energy-related domains. utilization AI techniques for addressing challenges is favored due their aptitude handling complex nonlinear data structures. Based on preliminary inquiries, it been observed that predictive analytics, prominently driven by artificial neural network (ANN) algorithms, assumes crucial position across sectors. This paper presents bibliometric analysis gain deeper insights into progression research from 2003 2023. models can be used accurately predict consumption, load profiles, resource planning, ensuring consistent performance utilization. review article summarizes existing literature development systems. Additionally, explores potential areas applying ANN system management. study demonstrates effectively address integration issues between power systems, such as solar wind forecasting, frequency control, transient stability assessment. state-of-the-art study, inferred consistently reductions exceeding 25%. Furthermore, this discusses future directions field.

Язык: Английский

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The blue treasure of hydrogen energy: A research of offshore wind power industry policy in China

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 62, С. 99 - 108

Опубликована: Март 11, 2024

Язык: Английский

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Renewable energy role in low-carbon economy and net-zero goal: Perspectives and prospects

Energy & Environment, Год журнала: 2024, Номер unknown

Опубликована: Май 21, 2024

Several issues such as sustainability, CO 2 footprint, and energy supply security which primarily resulted from fossil fuel emissions have become the main concerns for analysts policymakers worldwide. Therefore, to meet goals of sustainable well switch a net-zero low-carbon economy, systems must be diversified by increasing implementation renewable clean sources energy. This paper focused on deep analysis key role bioenergy, geothermal, solar, hydropower or hydrogen, ocean, wind (BIGSHOW) in producing aiming attain norms climate change mitigation. Furthermore, AI technology its applicability were also introduced enhance management efficiency BIGSHOW energy-use strategies. More importantly, barriers bottlenecks deploying projects applications comprehensively analyzed. Finally, policy implications vital solutions thoroughly presented increase penetration system. In short, this work could strong persuasive evidence speeding up shifting progress precarious fuel-based economy one, has been known core role.

Язык: Английский

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Track to reach net-zero: Progress and pitfalls

Energy & Environment, Год журнала: 2024, Номер unknown

Опубликована: Июнь 14, 2024

Over recent years, many companies and countries have established net-zero emission objectives for 2050 or sooner. Frankly, there will be fraught with challenges dangers to some extent attain net-zero. Therefore, we scrutinized the importance of strategies plans/roadmap these goals in this review. We found that overcoming diverse obstacles including settling on a formal definition concept, increasing global financing infrastructure investments, ensuring advancements green technology occur while keeping their costs low subsidizing them is very imperative quickly transition away from carbon-emitting fossil fuels. Other could include getting ball moving difficult-to-decarbonize sectors, choosing correct carbon offsets, not relying solely renewable energy credits, striking right balance between climate-related policies at various levels. Based review analysis, suggested solutions achieving by 2050, as well long-run scenarios. In short, all components sustainable development, socioeconomic sustainability, pursuit broad developing opportunities must matched emission-based economy, ensures stability harmony national targets international benefits.

Язык: Английский

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Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

JOIV International Journal on Informatics Visualization, Год журнала: 2024, Номер 8(1), С. 55 - 55

Опубликована: Март 16, 2024

Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, solar power, can revolutionize the industry. Biomass biofuels have benefited significantly from implementing AI ML algorithms that optimize feedstock, enhance resource management, facilitate biofuel production. By applying insight derived data analysis, stakeholders improve entire supply chain - biomass conversion, fuel synthesis, agricultural growth, harvesting to mitigate environmental impacts accelerate transition a low-carbon economy. Furthermore, in combustion systems engines has yielded substantial improvements efficiency, emissions reduction, overall performance. Enhancing engine design control techniques produces cleaner, more efficient minimal impact. This contributes sustainability of power generation transportation. are employed analyze vast quantities photovoltaic systems' design, operation, maintenance. The ultimate goal is increase output system efficiency. Collaboration among academia, industry, policymakers imperative expedite sustainable future harness potential energy. these technologies, it possible establish ecosystem, which would benefit generations.

Язык: Английский

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Development of advanced machine learning for prognostic analysis of drying parameters for banana slices using indirect solar dryer

Case Studies in Thermal Engineering, Год журнала: 2024, Номер 60, С. 104743 - 104743

Опубликована: Июнь 24, 2024

In this study, eXtreme Gradient Boosting (XGBoost) and Light (LightGBM) algorithms were used to model-predict the drying characteristics of banana slices with an indirect solar drier. The relationships between independent variables (temperature, moisture, product type, water flow rate, mass product) dependent (energy consumption size reduction) established. For energy consumption, XGBoost demonstrates superior performance R2 0.9957 during training 0.9971 testing, alongside minimal MSE 0.0034 0.0008 testing phase indicating high predictive accuracy low error rates. Conversely, LGBM shows lower values (0.9061 training, 0.8809 testing) higher 0.0747 0.0337 reflecting poorer performance. Similarly, for shrinkage prediction, outperforms LGBM, evidenced by (0.9887 0.9975 (0.2527 0.4878 testing). comparative statistics showed that regularly outperformed LightGBM. game theory-based Shapley functions revealed temperature types most influential features model. These findings illustrate practical applicability LightGBM models in food operations towards optimizing conditions, improving quality, reducing consumption.

Язык: Английский

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Experimental investigation on hydrogen-rich syngas production via gasification of common wood pellet in Bangladesh: Optimization, mathematical modeling, and techno-econo-environmental feasibility studies

Biomass Conversion and Biorefinery, Год журнала: 2024, Номер unknown

Опубликована: Июнь 28, 2024

Abstract Since hydrogen produces no emissions, there is increasing interest in its production throughout the world as need for clean and sustainable energy grows. Bangladesh has an abundance of biomass, particularly wood pellets, which presents a huge opportunity gasification to produce hydrogen. Gasification mahogany ( Swietenia mahagoni - SM) mango Mangifera indica- MI) performed downdraft gasifier evaluate impact particle size, equivalence ratio, temperature on gas composition performance. Under optimal conditions determined by central composite design-response surface methodology (CCD-RSM) optimization, SM MI can greatly increase yield cold efficiency, offering workable, environmentally friendly, long-term solution Bangladesh's shortage pollution problems. Through RSM analysis best operating include feed size 22.5 mm, ratio 0.34, 1176 K, where total 11.2% was obtained. In case gasification, optimum condition found at 1132.47 12.85% The economic study provides LCOE 0.1116 $/kWh, project payback period be 10.7 years. By reusing waste from nearby sawmills, this helps manage sustainably lowering levels deforestation. It also highlights wider sustainability effects assisting international initiatives fight climate change advance independence.

Язык: Английский

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