The rapid development of artificial intelligence(AI)technology,particularly breakthroughs in branches such as deep learning,reinforcement learning,and federated learning,has provided powerful technical tools for addre...The rapid development of artificial intelligence(AI)technology,particularly breakthroughs in branches such as deep learning,reinforcement learning,and federated learning,has provided powerful technical tools for addressing these core bottlenecks.This paper provides a systematic review of the research background,technological evolution,core systems,key challenges,and future directions of AI technology in the field of distributed photovoltaic power generation system optimization.At the same time,this paper analyzes the current technical bottlenecks and cutting-edge response strategies.Finally,it explores fusion innovation directions such as quantum-classical hybrid algorithms and neural symbolic systems,as well as business model expansion paths such as carbon finance integration and community energy autonomy.展开更多
The integration of artificial intelligence(AI)in aquaculture has been identified as a transformative force,enhancing various operational aspects from water quality management to genetic optimization.This review provid...The integration of artificial intelligence(AI)in aquaculture has been identified as a transformative force,enhancing various operational aspects from water quality management to genetic optimization.This review provides a comprehensive synthesis of recent advancements in AI applications within the aquaculture sector,underscoring the significant enhancements in production efficiency and environmental sustainability.Key AI-driven improvements,such as predictive analytics for disease management and optimized feeding protocols,are highlighted,demonstrating their contributions to reducing waste and improving biomass outputs.However,challenges remain in terms of data quality,system integration,and the socio-economic impacts of technological adoption across diverse aquacultural environments.This review also addresses the gaps in current research,particularly the lack of robust,scalable AI models and frameworks that can be universally applied.Future directions are discussed,emphasizing the need for interdisciplinary research and development to fully leverage AI potential in aquaculture.This study not only maps the current landscape of AI applications but also serves as a call for continued innovation and strategic collaborations to overcome existing barriers and realize the full benefits of AI in aquaculture.展开更多
The renewable hydrogen economy is recognized as an integral solution for decarbonizing energy sectors.However,high costs have hindered widespread deployment.One promising way of reducing the costs is optimization.Opti...The renewable hydrogen economy is recognized as an integral solution for decarbonizing energy sectors.However,high costs have hindered widespread deployment.One promising way of reducing the costs is optimization.Optimization generally involves finding the configuration of the renewable generation and hydrogen system components that maximizes return on investment.Previous studies have included many aspects into their optimizations,including technical parameters and different costs/socio-economic objective functions,however there is no clear best-practice framework for model development.To address these gaps,this critical review examines the latest development in renewable hydrogen microgrid models and summarizes the best modeling practice.The findings show that advances in machine learning integration are improving solar electricity generation forecasting,hydrogen system simulations,and load profile development,particularly in data-scarce regions.Additionally,it is important to account for electrolyzer and fuel cell dynamics,rather than utilizing fixed performance values.This review also demonstrates that typical meteorological year datasets are better for modeling solar irradiation than first-principle calculations.The practicability of socio-economic objective functions is also assessed,proposing that the more comprehensive Levelized Value Addition(LVA)is best suited for inclusion into models.Best practices for creating load profiles in regions like the Global South are discussed,along with an evaluation of AI-based and traditional optimization methods and software tools.Finally,a new evidence-based multi-criteria decision-making framework integrated with machine learning insights,is proposed to guide decision-makers in selecting optimal solutions based on multiple attributes,offering a more comprehensive and adaptive approach to renewable hydrogen system optimization.展开更多
A computing model employing the immune and genetic algorithm (IGA) for the optimization of part design is presented. This model operates on a population of points in search space simultaneously, not on just one point....A computing model employing the immune and genetic algorithm (IGA) for the optimization of part design is presented. This model operates on a population of points in search space simultaneously, not on just one point. It uses the objective function itself, not derivative or any other additional information and guarantees the fast convergence toward the global optimum. This method avoids some weak points in genetic algorithm, such as inefficient to some local searching problems and its convergence is too early. Based on this model, an optimal design support system (IGBODS) is developed.IGBODS has been used in practice and the result shows that this model has great advantage than traditional one and promises good application in optimal design.展开更多
文摘The rapid development of artificial intelligence(AI)technology,particularly breakthroughs in branches such as deep learning,reinforcement learning,and federated learning,has provided powerful technical tools for addressing these core bottlenecks.This paper provides a systematic review of the research background,technological evolution,core systems,key challenges,and future directions of AI technology in the field of distributed photovoltaic power generation system optimization.At the same time,this paper analyzes the current technical bottlenecks and cutting-edge response strategies.Finally,it explores fusion innovation directions such as quantum-classical hybrid algorithms and neural symbolic systems,as well as business model expansion paths such as carbon finance integration and community energy autonomy.
基金funding sources,including the National Natural Science Foundation of China(No.51709254,No.32201384)Youth Innovation Promotion Association,Chinese Academy of Sciences(No.2020335)+1 种基金Key Research and Development Program of Hubei Province,China(2020BCA073)National Science&Technology Fundamental Resources Investigation Program of China(2019FY100602).
文摘The integration of artificial intelligence(AI)in aquaculture has been identified as a transformative force,enhancing various operational aspects from water quality management to genetic optimization.This review provides a comprehensive synthesis of recent advancements in AI applications within the aquaculture sector,underscoring the significant enhancements in production efficiency and environmental sustainability.Key AI-driven improvements,such as predictive analytics for disease management and optimized feeding protocols,are highlighted,demonstrating their contributions to reducing waste and improving biomass outputs.However,challenges remain in terms of data quality,system integration,and the socio-economic impacts of technological adoption across diverse aquacultural environments.This review also addresses the gaps in current research,particularly the lack of robust,scalable AI models and frameworks that can be universally applied.Future directions are discussed,emphasizing the need for interdisciplinary research and development to fully leverage AI potential in aquaculture.This study not only maps the current landscape of AI applications but also serves as a call for continued innovation and strategic collaborations to overcome existing barriers and realize the full benefits of AI in aquaculture.
基金financial support offered to M.D.M.for his doctoral research by the UK Engineering and Physical Sciences Research Council and Loughborough University through the EPSRC Sustainable Hydrogen Centre for Doctoral Training funded by the UK Research and Innovation(UKRI)[grant number EP/S023909/1].
文摘The renewable hydrogen economy is recognized as an integral solution for decarbonizing energy sectors.However,high costs have hindered widespread deployment.One promising way of reducing the costs is optimization.Optimization generally involves finding the configuration of the renewable generation and hydrogen system components that maximizes return on investment.Previous studies have included many aspects into their optimizations,including technical parameters and different costs/socio-economic objective functions,however there is no clear best-practice framework for model development.To address these gaps,this critical review examines the latest development in renewable hydrogen microgrid models and summarizes the best modeling practice.The findings show that advances in machine learning integration are improving solar electricity generation forecasting,hydrogen system simulations,and load profile development,particularly in data-scarce regions.Additionally,it is important to account for electrolyzer and fuel cell dynamics,rather than utilizing fixed performance values.This review also demonstrates that typical meteorological year datasets are better for modeling solar irradiation than first-principle calculations.The practicability of socio-economic objective functions is also assessed,proposing that the more comprehensive Levelized Value Addition(LVA)is best suited for inclusion into models.Best practices for creating load profiles in regions like the Global South are discussed,along with an evaluation of AI-based and traditional optimization methods and software tools.Finally,a new evidence-based multi-criteria decision-making framework integrated with machine learning insights,is proposed to guide decision-makers in selecting optimal solutions based on multiple attributes,offering a more comprehensive and adaptive approach to renewable hydrogen system optimization.
文摘A computing model employing the immune and genetic algorithm (IGA) for the optimization of part design is presented. This model operates on a population of points in search space simultaneously, not on just one point. It uses the objective function itself, not derivative or any other additional information and guarantees the fast convergence toward the global optimum. This method avoids some weak points in genetic algorithm, such as inefficient to some local searching problems and its convergence is too early. Based on this model, an optimal design support system (IGBODS) is developed.IGBODS has been used in practice and the result shows that this model has great advantage than traditional one and promises good application in optimal design.
