Studies quantifying AI's impact on Sustainable Development Goals(SDGs)often rely on proxies that inac-curately reflect AI progress.Moreover,focusing solely on environmental and growth indicators provides an incomp...Studies quantifying AI's impact on Sustainable Development Goals(SDGs)often rely on proxies that inac-curately reflect AI progress.Moreover,focusing solely on environmental and growth indicators provides an incomplete picture of AI's overall contribution to the SDGs,as the SDG framework encompasses a broader set of interconnected goals.Therefore,this study unveils the marginal impacts of AI and solar energy(SEN)directly on the SDG Index(SDGI)by using the Kernel-Based Regularized Least Squares(KRLS)machine learning approach for the 10 largest economies from 2000 2022.While the study found an overall positive average marginal impact of AI on the SDG Index,indicating significant progress driven by AI technologies,the analysis across different quantiles revealed variability.Specifically,at the 25th quantile,AI appears to hinder SDG progress.This could be due to negative externalities from AI adoption,like its use in accelerating non-renewable energy production and resource-intensive consumption,or from countries'insufficient technological application capabilities.However,at higher quantiles(likely representing countries with better SDG achievement and greater AI maturity),the marginal effects of AI become increasingly positive,suggesting its beneficial use in areas that support SDGs.Marginal effects of SEN on SDGI are found to be positive,showing a positive connection between SDGs' achievements and solar energy adoption.The marginal effects of economic globalization(EGB)and institutional productive capacity(INP)on SDGI are found to be positive.Finally,policies to boost AI and solar energy adoption,as well as exploring potential applications of AI across various sectors for sustainable development,are discussed.展开更多
Satellite remote sensing is essential for solar energy meteorology.The 14-channel Advanced Geostationary Radiation Imager of the Fengyun-4 series of satellites performs a full-disc scan over greater China every 15 min...Satellite remote sensing is essential for solar energy meteorology.The 14-channel Advanced Geostationary Radiation Imager of the Fengyun-4 series of satellites performs a full-disc scan over greater China every 15 min,providing highgranularity information that allows the retrieval of cloud properties,aerosol optical depth,and precipitable water vapor content,which can facilitate the acquisition of surface solar irradiance components through physical methods.Machinelearning methods have also shown potential in providing accurate end-to-end surface solar radiation retrievals.Albeit the physical principles of irradiance retrieval and machine-learning algorithms are fairly well known,the public service concerning disseminating the irradiance product to the energy and power industry still lacks robustness and consistency.In this perspective article,the status quo of Fengyun-4 irradiance products is first reviewed.Then,from the perspective of solar resource assessment and forecasting,three fundamental characteristics of the kind of irradiance products that are most serviceable to the solar energy sector are identified,namely,coverage,timeliness,and accessibility.Finally,an outlook on the new-generation Fengyun radiation service is put forward,and the prospective scientific and practical challenges are elaborated.展开更多
Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting,where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradi...Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting,where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradiance to solar power production.Ensemble simulations from such weather models aim to quantify uncertainty in the future development of the weather,and can be used to propagate this uncertainty through the model chain to generate probabilistic solar energy predictions.However,ensemble prediction systems are known to exhibit systematic errors,and thus require post-processing to obtain accurate and reliable probabilistic forecasts.The overarching aim of our study is to systematically evaluate different strategies to apply post-processing in model chain approaches with a specific focus on solar energy:not applying any post-processing at all;post-processing only the irradiance predictions before the conversion;post-processing only the solar power predictions obtained from the model chain;or applying post-processing in both steps.In a case study based on a benchmark dataset for the Jacumba solar plant in the U.S.,we develop statistical and machine learning methods for postprocessing ensemble predictions of global horizontal irradiance(GHI)and solar power generation.Further,we propose a neural-network-based model for direct solar power forecasting that bypasses the model chain.Our results indicate that postprocessing substantially improves the solar power generation forecasts,in particular when post-processing is applied to the power predictions.The machine learning methods for post-processing slightly outperform the statistical methods,and the direct forecasting approach performs comparably to the post-processing strategies.展开更多
The increasing demand due to development and advancement in every field of life has caused the depletion of fossil fuels.This depleting fossil fuel reserve throughout the world has enforced to get energy from alternat...The increasing demand due to development and advancement in every field of life has caused the depletion of fossil fuels.This depleting fossil fuel reserve throughout the world has enforced to get energy from alternativeenewable sources.One of the economicalways to get energy is through the utilization of solar ponds.In this study,a mathematical model of a salt gradient solar pond under the Islamabad climatic conditions has been analyzed for the first time.The model uses a one-dimensional finite difference explicit method for optimization of different zone thicknesses.The model depicts that NCZ(Non-Convective Zone)thickness has a significant effect on LCZ(Lower Convective Zone)temperature and should be kept less than 1.7mfor the optimal temperature.It is also observed that for long-termoperation of a solar pond,heat should be extracted by keeping the mass flowrate of 17.3 kg/m2/day.Themodel also suggests that when the bottom reflectivity is about 0.3,then only 24%of the radiation is absorbed in the pond.展开更多
Amidst the global push for decarbonization,solar-powered Organic Rankine Cycle(SORC)systems are gaining significant attention.The small-scale Organic Rankine Cycle(ORC)systems have enhanced environmental adaptability,...Amidst the global push for decarbonization,solar-powered Organic Rankine Cycle(SORC)systems are gaining significant attention.The small-scale Organic Rankine Cycle(ORC)systems have enhanced environmental adaptability,improved system flexibility,and achieved diversification of application scenarios.However,the power consumption ratio of the working fluid pump becomes significantly larger relative to the total power output of the system,adversely impacting overall system efficiency.This study introduces an innovative approach by incorporating a vapor-liquid ejector into the ORC system to reduce the pump work consumption within the ORC.The thermoeconomic models for both the traditional ORC and an ORC integrated with a vapor-liquid ejector driven by solar parabolic trough collectors(PTCs)were developed.Key evaluation indicators,such as thermal efficiency,exergy efficiency,specific investment cost,and levelized cost of energy,were employed to compare the SORC with the solar ejector organic Rankine cycle(SEORC).Additionally,the study explores the effects of solar beam radiation intensity,PTC temperature variation,evaporator pinch point temperature difference,and condenser pinch point temperature difference on the thermo-economic performance of both systems.Results demonstrate that SEORC consistently outperforms SORC.Higher solar radiation intensity and increased PTC inlet temperature lead to better system efficiency.Moreover,there is an optimal PTC temperature drop where both thermal and exergy efficiencies are maximized.The influence of evaporator and condenser temperature pinches on system performance is found to be inconsistent.展开更多
摘要Studies quantifying AI's impact on Sustainable Development Goals(SDGs)often rely on proxies that inac-curately reflect AI progress.Moreover,focusing solely on environmental and growth indicators provides an incomplete picture of AI's overall contribution to the SDGs,as the SDG framework encompasses a broader set of interconnected goals.Therefore,this study unveils the marginal impacts of AI and solar energy(SEN)directly on the SDG Index(SDGI)by using the Kernel-Based Regularized Least Squares(KRLS)machine learning approach for the 10 largest economies from 2000 2022.While the study found an overall positive average marginal impact of AI on the SDG Index,indicating significant progress driven by AI technologies,the analysis across different quantiles revealed variability.Specifically,at the 25th quantile,AI appears to hinder SDG progress.This could be due to negative externalities from AI adoption,like its use in accelerating non-renewable energy production and resource-intensive consumption,or from countries'insufficient technological application capabilities.However,at higher quantiles(likely representing countries with better SDG achievement and greater AI maturity),the marginal effects of AI become increasingly positive,suggesting its beneficial use in areas that support SDGs.Marginal effects of SEN on SDGI are found to be positive,showing a positive connection between SDGs' achievements and solar energy adoption.The marginal effects of economic globalization(EGB)and institutional productive capacity(INP)on SDGI are found to be positive.Finally,policies to boost AI and solar energy adoption,as well as exploring potential applications of AI across various sectors for sustainable development,are discussed.
基金supported by the National Natural Science Foundation of China(project no.42375192)。
摘要Satellite remote sensing is essential for solar energy meteorology.The 14-channel Advanced Geostationary Radiation Imager of the Fengyun-4 series of satellites performs a full-disc scan over greater China every 15 min,providing highgranularity information that allows the retrieval of cloud properties,aerosol optical depth,and precipitable water vapor content,which can facilitate the acquisition of surface solar irradiance components through physical methods.Machinelearning methods have also shown potential in providing accurate end-to-end surface solar radiation retrievals.Albeit the physical principles of irradiance retrieval and machine-learning algorithms are fairly well known,the public service concerning disseminating the irradiance product to the energy and power industry still lacks robustness and consistency.In this perspective article,the status quo of Fengyun-4 irradiance products is first reviewed.Then,from the perspective of solar resource assessment and forecasting,three fundamental characteristics of the kind of irradiance products that are most serviceable to the solar energy sector are identified,namely,coverage,timeliness,and accessibility.Finally,an outlook on the new-generation Fengyun radiation service is put forward,and the prospective scientific and practical challenges are elaborated.
基金the Young Investigator Group“Artificial Intelligence for Probabilistic Weather Forecasting”funded by the Vector Stiftungfunding from the Federal Ministry of Education and Research(BMBF)and the Baden-Württemberg Ministry of Science as part of the Excellence Strategy of the German Federal and State Governments。
摘要Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting,where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradiance to solar power production.Ensemble simulations from such weather models aim to quantify uncertainty in the future development of the weather,and can be used to propagate this uncertainty through the model chain to generate probabilistic solar energy predictions.However,ensemble prediction systems are known to exhibit systematic errors,and thus require post-processing to obtain accurate and reliable probabilistic forecasts.The overarching aim of our study is to systematically evaluate different strategies to apply post-processing in model chain approaches with a specific focus on solar energy:not applying any post-processing at all;post-processing only the irradiance predictions before the conversion;post-processing only the solar power predictions obtained from the model chain;or applying post-processing in both steps.In a case study based on a benchmark dataset for the Jacumba solar plant in the U.S.,we develop statistical and machine learning methods for postprocessing ensemble predictions of global horizontal irradiance(GHI)and solar power generation.Further,we propose a neural-network-based model for direct solar power forecasting that bypasses the model chain.Our results indicate that postprocessing substantially improves the solar power generation forecasts,in particular when post-processing is applied to the power predictions.The machine learning methods for post-processing slightly outperform the statistical methods,and the direct forecasting approach performs comparably to the post-processing strategies.
摘要The increasing demand due to development and advancement in every field of life has caused the depletion of fossil fuels.This depleting fossil fuel reserve throughout the world has enforced to get energy from alternativeenewable sources.One of the economicalways to get energy is through the utilization of solar ponds.In this study,a mathematical model of a salt gradient solar pond under the Islamabad climatic conditions has been analyzed for the first time.The model uses a one-dimensional finite difference explicit method for optimization of different zone thicknesses.The model depicts that NCZ(Non-Convective Zone)thickness has a significant effect on LCZ(Lower Convective Zone)temperature and should be kept less than 1.7mfor the optimal temperature.It is also observed that for long-termoperation of a solar pond,heat should be extracted by keeping the mass flowrate of 17.3 kg/m2/day.Themodel also suggests that when the bottom reflectivity is about 0.3,then only 24%of the radiation is absorbed in the pond.
基金This research was funded by Natural Science Foundation of Guangdong Province,grant number 2024A1515030130National Natural Science Foundation of China,grant number 42102336.
摘要Amidst the global push for decarbonization,solar-powered Organic Rankine Cycle(SORC)systems are gaining significant attention.The small-scale Organic Rankine Cycle(ORC)systems have enhanced environmental adaptability,improved system flexibility,and achieved diversification of application scenarios.However,the power consumption ratio of the working fluid pump becomes significantly larger relative to the total power output of the system,adversely impacting overall system efficiency.This study introduces an innovative approach by incorporating a vapor-liquid ejector into the ORC system to reduce the pump work consumption within the ORC.The thermoeconomic models for both the traditional ORC and an ORC integrated with a vapor-liquid ejector driven by solar parabolic trough collectors(PTCs)were developed.Key evaluation indicators,such as thermal efficiency,exergy efficiency,specific investment cost,and levelized cost of energy,were employed to compare the SORC with the solar ejector organic Rankine cycle(SEORC).Additionally,the study explores the effects of solar beam radiation intensity,PTC temperature variation,evaporator pinch point temperature difference,and condenser pinch point temperature difference on the thermo-economic performance of both systems.Results demonstrate that SEORC consistently outperforms SORC.Higher solar radiation intensity and increased PTC inlet temperature lead to better system efficiency.Moreover,there is an optimal PTC temperature drop where both thermal and exergy efficiencies are maximized.The influence of evaporator and condenser temperature pinches on system performance is found to be inconsistent.