This study presents a comprehensive evaluation of tropical cyclone(TC)forecast performance in the western North Pacific from 2013 to 2022,based on operational forecasts issued by the China Meteorological Administratio...This study presents a comprehensive evaluation of tropical cyclone(TC)forecast performance in the western North Pacific from 2013 to 2022,based on operational forecasts issued by the China Meteorological Administration.The analysis reveals systematic improvements in both track and intensity forecasts over the decade,with distinct error characteristics observed across various forecast parameters.Track forecast errors have steadily decreased,particularly for longer lead times,while error magnitudes have increased with longer forecast lead times.Intensity forecasts show similar progressive enhancements,with maximum sustained wind speed errors decreasing by 0.26 m/s per year for 120 h forecasts.The study also identifies several key patterns in forecast performance:typhoon-grade or stronger TCs exhibit smaller track errors than week or weaker systems;intensity forecasts systematically overestimate weaker TCs while underestimating stronger systems;and spatial error distributions show greater track inaccuracies near landmasses and regional intensity biases.These findings highlight both the significant advances in TC forecasting capability achieved through improved modeling and observational systems,and the remaining challenges in predicting TC changes and landfall behavior,providing valuable benchmarks for future forecast system development.展开更多
The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth’s ionosphere,affecting communication and na...The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth’s ionosphere,affecting communication and navigation systems.Consequently,accurately predicting the intensity of the SC holds great significance,but predicting the SC involves a long-term time series,and many existing time series forecasting methods have fallen short in terms of accuracy and efficiency.The Time-series Dense Encoder model is a deep learning solution tailored for long time series prediction.Based on a multi-layer perceptron structure,it outperforms the best previously existing models in accuracy,while being efficiently trainable on general datasets.We propose a method based on this model for SC forecasting.Using a trained model,we predict the test set from SC 19 to SC 25 with an average mean absolute percentage error of 32.02,root mean square error of 30.3,mean absolute error of 23.32,and R^(2)(coefficient of determination)of 0.76,outperforming other deep learning models in terms of accuracy and training efficiency on sunspot number datasets.Subsequently,we use it to predict the peaks of SC 25 and SC 26.For SC 25,the peak time has ended,but a stronger peak is predicted for SC 26,of 199.3,within a range of 170.8-221.9,projected to occur during April 2034.展开更多
Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning ...Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.展开更多
Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep...Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.展开更多
With the rapid proliferation of electric vehicles,their charging loads pose new challenges to power grid stability and operational efficiency.To address this,this study employs a Monte Carlo simulation model to analyz...With the rapid proliferation of electric vehicles,their charging loads pose new challenges to power grid stability and operational efficiency.To address this,this study employs a Monte Carlo simulation model to analyze the charging load characteristics of six battery electric vehicle categories in Hebei Province,leveraging multi-source probabilistic distribution data under typical operational scenarios.The findings reveal that electric vehicle charging loads are primarily concentrated during midday and nighttime periods,with significant load fluctuations exerting substantial pressure on the grid.In response,this paper proposes strategic interventions including optimized charging infrastructure planning,time-of-use electricity pricing mechanisms,and smart charging technologies to balance grid loads.The results provide a theoretical foundation for electric vehicle load forecasting,smart grid dispatching,and vehicle-grid integration,thereby enhancing grid operational efficiency and sustainability.展开更多
Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The prese...Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.展开更多
Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-iti...Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-ities where operational records are scarce.This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such“cold-start”forecasting problems.It primarily covers three interrelated domains—solar photovoltaic(PV),wind power,and electrical load forecasting—where data scarcity and operational variability are most critical,while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective.To this end,we examined trends from over 150 predominantly peer-reviewed studies published between 2019 and mid-2025,highlighting advances in zero-shot and few-shot meta-learning frameworks that enable rapid model adaptation with minimal labeled data.Moreover,transfer learning approaches combined with spatiotemporal graph neural networks have been employed to transfer knowledge from existing energy assets to new,data-sparse environments,effectively capturing hidden dependencies among geographic features,meteorological dynamics,and grid structures.Synthetic data generation has further proven valuable for expanding training samples and mitigating overfitting in cold-start scenarios.In addition,large language models and explainable artificial intelligence(XAI)—notably conversational XAI systems—have been used to interpret and communicate complex model behaviors in accessible terms,fostering operator trust from the earliest deployment stages.By consolidating methodological advances,unresolved challenges,and open-source resources,this review provides a coherent overview of deep learning strategies that can shorten the data-sparse ramp-up period of new energy infrastructures and accelerate the transition toward resilient,low-carbon electricity grids.展开更多
It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using...It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using an identical reference.In this study,three physically reasonable DLMs are implemented for the forecasting of the sea surface temperature(SST),sea level anomaly(SLA),and sea surface velocity in the South China Sea.The DLMs are validated against both the testing dataset and the“OceanPredict”Class 4 dataset.Results show that the DLMs'RMSEs against the latter increase by 44%,245%,302%,and 109%for SST,SLA,current speed,and direction,respectively,compared to those against the former.Therefore,different references have significant influences on the validation,and it is necessary to use an identical and independent reference to intercompare the DLMs and OFSs.Against the Class 4 dataset,the DLMs present significantly better performance for SLA than the OFSs,and slightly better performances for other variables.The error patterns of the DLMs and OFSs show a high degree of similarity,which is reasonable from the viewpoint of predictability,facilitating further applications of the DLMs.For extreme events,the DLMs and OFSs both present large but similar forecast errors for SLA and current speed,while the DLMs are likely to give larger errors for SST and current direction.This study provides an evaluation of the forecast skills of commonly used DLMs and provides an example to objectively intercompare different DLMs.展开更多
Marine heatwaves(MHWs),which can exert devastating socioeconomic and ecological impacts,have attracted much public interest in recent years.In this study,we evaluate the sub-seasonal forecast skill of MHWs based on th...Marine heatwaves(MHWs),which can exert devastating socioeconomic and ecological impacts,have attracted much public interest in recent years.In this study,we evaluate the sub-seasonal forecast skill of MHWs based on the Nanjing University of Information Science&Technology Climate Forecast System version 1.1(NUIST CFS1.1)and analyze the related physical processes.Our results show that the model can accurately forecast the occurrence of MHWs on a global scale out to a lead time of 25 days.Notably,even at lead times of 51–55 days,the forecast skill in most tropical regions,as well as in the northeastern and southeastern Pacific,is superior to both random forecasts and persistence forecasts.Accurate predictions of sea level pressure,zonal currents,and mixed-layer depth are important for MHW forecasting.Furthermore,we also conduct forecast skill assessments for two well-documented MHW events.Due to its ability to correctly forecast the changes in heat flux anomalies at a lead time of 25 days,the model can accurately forecast the strong MHW event that occurred in the South China Sea in May–October 2020.However,the forecasting results were less than optimal for the strong MHW event that occurred along the Australian west coast in January–April 2011.Although the model accurately forecasts its occurrence,the forecast of its intensity is poor.Additionally,when the lead time exceeds 10 days,forecasts of the relevant physical processes of this MHW event are also inaccurate.展开更多
The rapid development of technology has led to an ever-increasing demand for electrical energy.In the context of Timor-Leste,which still relies on fossil energy sources with high operational costs and significant envi...The rapid development of technology has led to an ever-increasing demand for electrical energy.In the context of Timor-Leste,which still relies on fossil energy sources with high operational costs and significant environmental impacts,electricity load forecasting is a strategic measure to support the energy transition towards the Net Zero Emission(NZE)target by 2050.This study aims to utilize historical electricity load data for the period 2013–2024,as well as data on external factors affecting electricity consumption,to forecast electricity load in Timor-Leste in the next 10 years(2025–2035).The forecasting results are expected to support efforts in energy distribution efficiency,reduce operational costs,and inform decisions related to the sustainable energy transition.The method used in this study consists of two main approaches:the causality method,represented by the econometric Principal Component Analysis(PCA)model,which involves external factors in the data processing process,and the time series method,utilizing the LSTM,XGBoost,and hybrid(LSTM+XGBoost)models.In the time series method,data processing is combined with two approaches:the sliding window and the rolling recursive forecast.The performance of each model is evaluated using the Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Mean Absolute Percentage Error(MAPE).The model with the lowest MAPE(<10%)is considered the best-performing model,indicating the highest accuracy.Additionally,a Monte Carlo simulation with 50,000 iterations was used to process the data and measure the prediction uncertainty,as well as test the calibration of the electricity load projection data.The results showed that the hybrid model(LSTM+XGBoost)with a rolling forecast recursive approach is the best-performing model in predicting electricity load in Timor-Leste.This model yields an RMSE of 75.76 MW,an MAE of 55.76 MW,and an MAPE of 5.27%,indicating a high level of accuracy.In addition,the model is also indicated as one that fits the characteristics of electricity load in Timor-Leste,as it produces the lowest percentage of forecasting error in predicting electricity load.The integration of the best model with Monte Carlo Simulation,which yields a p-value of 0.565,suggests that the results of electricity load projections for the period 2025–2035 are well-calibrated,reliable,accurate,and unbiased.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
In this study,we investigate the extent to which sustainability disclosures in the narrative sections of European banks’annual reports improve analysts’forecasting accuracy.We capture sustainability disclosures with...In this study,we investigate the extent to which sustainability disclosures in the narrative sections of European banks’annual reports improve analysts’forecasting accuracy.We capture sustainability disclosures with a machine learning approach and use forecast errors as a proxy for analysts’forecast accuracy.Our results suggest that sustainability disclosures significantly improve analysts’forecasting accuracy by reducing forecast errors.In a further analysis,we also find that the introduction of Directive 2014/95/European Union is associated with increased disclosure content,which reduces forecast error.Collectively,our results suggest that sustainability disclosures improve forecast accuracy,and the introduction of the new EU directive strengthens this improvement.These results hold after several robustness tests.Our findings have important implications for market participants and policymakers.展开更多
As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitat...As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitations in handling nonlinear and high-dimensional data,while neural networks(NNs)have demonstrated great potential due to their powerful feature extraction and pattern recognition capabilities.Although several existing surveys discuss the applications of NNs in stock forecasting,they often lack a detailed examination of models that use time-series data as input and fail to cover the latest research developments.In response,this paper reviews relevant literature from 2015 to 2025 and classifies timeseriesbased stock forecasting methods into four categories:NNs,recurrent NNs(RNNs),convolutional NNs(CNNs),Transformers and other models.We analyze their performance under different market conditions,highlight strengths and limitations,and identify recent trends in model design.Our findings show that hybrid architectures and attention-based models consistently achieve superior forecasting stability and adaptability across volatile market scenarios.This survey offers a systematic reference for researchers and practitioners and outlines promising future research directions.展开更多
The China Meteorological Administration(CMA)said that in the last five years,China has made big improvements in its weather services.This includes better weather forecasts and ways to protect people from disasters.
According to the demand for weather forecast at the venues of the 14 th National Winter Games,based on the data of the fine grid model of the European Centre(EC)and RMAPS model,as well as the real-time observation dat...According to the demand for weather forecast at the venues of the 14 th National Winter Games,based on the data of the fine grid model of the European Centre(EC)and RMAPS model,as well as the real-time observation data of the competition fields,a dynamic optimal correction method was proposed to improve the accuracy rate of temperature and wind speed prediction.Through techniques such as deviation correction and univariate linear regression,mathematical models applicable to different competition regions were constructed,and the effective correction of objective forecast products within 0-120 h were realized.The results show that this method significantly improved the accuracy rate of the prediction of temperature,wind speed and extreme wind speed,and the effect was more obvious especially when the model performance was unstable.Meanwhile,terrain and climate background had a significant impact on the correction effect.This study provides new technical support for mountain meteorological forecast.展开更多
This study focuses on an extreme rainfall event in East China during the mei-yu season,in which the capital city(Nanjing)of Jiangsu Province experienced a maximum 14-h rainfall accumulation of 209.6 mm and a peak hour...This study focuses on an extreme rainfall event in East China during the mei-yu season,in which the capital city(Nanjing)of Jiangsu Province experienced a maximum 14-h rainfall accumulation of 209.6 mm and a peak hourly rainfall of 118.8 mm.The performance of two sets of convection-permitting ensemble forecast systems(CEFSs),each with 30 members and a 3-km horizontal grid spacing,is evaluated.The CEFS_ICBCs,using multiple initial and boundary conditions(ICs and BCs),and the CEFS_ICBCs Phys,which incorporates both multi-physics schemes and ICs/BCs,are compared to the CMA-REPS(China Meteorological Administration-Regional Ensemble Prediction System)with a coarser 10-km grid spacing.The two CEFSs demonstrate more uniform rank histograms and lower Brier scores(with higher resolution),improving precipitation intensity predictions and providing more reliable probability forecasts,although they overestimate precipitation over Mt.Dabie.It is challenging for the CEFSs to capture the evolution of mesoscale rainstorms that are known to be related to the errors in predicting the southwesterly low-level winds.Sensitivity experiments reveal that the microphysics and radiation schemes introduce considerable uncertainty in predicting the intensity and location of heavy rainfall in and near Nanjing and Mt.Dabie.In particular,the Asymmetric Convection Model 2(ACM2)planetary boundary layer scheme combined with the Pleim-Xiu surface layer scheme tends to produce a biased northeastward extension of the boundary-layer jet,contributing to the northeastward bias of heavy precipitation around Nanjing in the CEFS_ICBCs.展开更多
Subseasonal forecasting of extreme events is crucial for early warning systems.However,the forecast skills for extreme events are limited.Taking the extreme cold events in January 2018 as a specific example,and analyz...Subseasonal forecasting of extreme events is crucial for early warning systems.However,the forecast skills for extreme events are limited.Taking the extreme cold events in January 2018 as a specific example,and analyzing the 34 extreme cold events in East Asia from 1998 to 2020,the authors evaluated the forecast skills of the ECMWF model ensemble members on subseasonal time scales.The results show that while the ensemble mean has limited skills for forecasting extreme cold events at the 3-week lead time,some individual members demonstrate high forecast skills.For most extreme cold events,there are>10%of members among the total ensembles that can well predict the rapid temperature transitions at the 14-day lead time.This highlights the untapped potential of the ECMWF model to forecast extreme cold events on subseasonal time scales.High-skill ensemble members rely on accurate predictions of atmospheric circulation patterns(500-hPa geopotential height,mean sea level pressure)and key weather systems,including the Ural Blocking and Siberian High,that influence extreme cold events.展开更多
The dynamics of vapor−liquid−solid(V−L−S)flow boiling in fluidized bed evaporators exhibit inherent complexity and chaotic behavior,hindering accurate prediction of pressure drop signals.To address this challenge,this...The dynamics of vapor−liquid−solid(V−L−S)flow boiling in fluidized bed evaporators exhibit inherent complexity and chaotic behavior,hindering accurate prediction of pressure drop signals.To address this challenge,this study proposes an innovative hybrid approach that integrates wavelet neural network(WNN)with chaos analysis.By leveraging the Cross-Correlation(C−C)method,the minimum embedding dimension for phase space reconstruction is systematically calculated and then adopted as the input node configuration for the WNN.Simulation results demonstrate the remarkable effectiveness of this integrated method in predicting pressure drop signals,advancing our understanding of the intricate dynamic phenomena occurring with V−L−S fluidized bed evaporators.Moreover,this study offers a novel perspective on applying advanced data-driven techniques to handle the complexities of multi-phase flow systems and highlights the potential for improved operational prediction and control in industrial settings.展开更多
The significance of accurately forecasting natural gas prices is far-reaching and significant,not only for the stable operation of the energy market,but also as a key element in promoting sustainable development and a...The significance of accurately forecasting natural gas prices is far-reaching and significant,not only for the stable operation of the energy market,but also as a key element in promoting sustainable development and addressing environmental challenges.However,natural gas prices are affected by multiple source factors,presenting complex,unstable nonlinear characteristics hindering the improvement of the prediction accuracy of existing models.To address this issue,this study proposes an innovative multivariate combined forecasting model for natural gas prices.Initially,the study meticulously identifies and introduces 16 variables impacting natural gas prices across five crucial dimensions:the production,marketing,commodities,political and economic indicators of the United States and temperature.Subsequently,this study employs the least absolute shrinkage and selection operator,grey relation analysis,and random forest for dimensionality reduction,effectively screening out the most influential key variables to serve as input features for the subsequent learning model.Building upon this foundation,a suite of machine learning models is constructed to ensure precise natural gas price prediction.To further elevate the predictive performance,an intelligent algorithm for parameter optimization is incorporated,addressing potential limitations of individual models.To thoroughly assess the prediction accuracy of the proposed model,this study conducts three experiments using monthly natural gas trading prices.These experiments incorporate 19 benchmark models for comparative analysis,utilizing five evaluation metrics to quantify forecasting effectiveness.Furthermore,this study conducts in-depth validation of the proposed model's effectiveness through hypothesis testing,discussions on the improvement ratio of forecasting performance,and case studies on other energy prices.The empirical results demonstrate that the multivariate combined forecasting method developed in this study surpasses other comparative models in forecasting accuracy.It offers new perspectives and methodologies for natural gas price forecasting while also providing valuable insights for other energy price forecasting studies.展开更多
Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environment...Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environmental footprint by reducing the risks of disruption,downtime,and waste.However,with increasingly complex energy consumption patterns driven by renewable energy integration and changing consumer behaviors,no single approach has emerged as universally effective.In response,this research presents a hybrid modeling framework that combines the strengths of Random Forest(RF)and Autoregressive Integrated Moving Average(ARIMA)models,enhanced with advanced feature selection—Minimum Redundancy Maximum Relevancy and Maximum Synergy(MRMRMS)method—to produce a sparse model.Additionally,the residual patterns are analyzed to enhance forecast accuracy.High-resolution weather data from Weather Underground and historical energy consumption data from PJM for Duke Energy Ohio and Kentucky(DEO&K)are used in this application.This methodology,termed SP-RF-ARIMA,is evaluated against existing approaches;it demonstrates more than 40%reduction in mean absolute error and root mean square error compared to the second-best method.展开更多
基金supported by the National Key R&D Program of China [grant number 2023YFC3008004]。
文摘This study presents a comprehensive evaluation of tropical cyclone(TC)forecast performance in the western North Pacific from 2013 to 2022,based on operational forecasts issued by the China Meteorological Administration.The analysis reveals systematic improvements in both track and intensity forecasts over the decade,with distinct error characteristics observed across various forecast parameters.Track forecast errors have steadily decreased,particularly for longer lead times,while error magnitudes have increased with longer forecast lead times.Intensity forecasts show similar progressive enhancements,with maximum sustained wind speed errors decreasing by 0.26 m/s per year for 120 h forecasts.The study also identifies several key patterns in forecast performance:typhoon-grade or stronger TCs exhibit smaller track errors than week or weaker systems;intensity forecasts systematically overestimate weaker TCs while underestimating stronger systems;and spatial error distributions show greater track inaccuracies near landmasses and regional intensity biases.These findings highlight both the significant advances in TC forecasting capability achieved through improved modeling and observational systems,and the remaining challenges in predicting TC changes and landfall behavior,providing valuable benchmarks for future forecast system development.
基金supported by the Academic Research Projects of Beijing Union University(ZK20202204)the National Natural Science Foundation of China(12250005,12073040,12273059,11973056,12003051,11573037,12073041,11427901,11572005,11611530679 and 12473052)+1 种基金the Strategic Priority Research Program of the China Academy of Sciences(XDB0560000,XDA15052200,XDB09040200,XDA15010700,XDB0560301,and XDA15320102)the Chinese Meridian Project(CMP).
文摘The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth’s ionosphere,affecting communication and navigation systems.Consequently,accurately predicting the intensity of the SC holds great significance,but predicting the SC involves a long-term time series,and many existing time series forecasting methods have fallen short in terms of accuracy and efficiency.The Time-series Dense Encoder model is a deep learning solution tailored for long time series prediction.Based on a multi-layer perceptron structure,it outperforms the best previously existing models in accuracy,while being efficiently trainable on general datasets.We propose a method based on this model for SC forecasting.Using a trained model,we predict the test set from SC 19 to SC 25 with an average mean absolute percentage error of 32.02,root mean square error of 30.3,mean absolute error of 23.32,and R^(2)(coefficient of determination)of 0.76,outperforming other deep learning models in terms of accuracy and training efficiency on sunspot number datasets.Subsequently,we use it to predict the peaks of SC 25 and SC 26.For SC 25,the peak time has ended,but a stronger peak is predicted for SC 26,of 199.3,within a range of 170.8-221.9,projected to occur during April 2034.
文摘Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.
基金supported by the National Natural Science Foundation of China[grant number 62376217]the Young Elite Scientists Sponsorship Program by CAST[grant number 2023QNRC001]the Joint Research Project for Meteorological Capacity Improvement[grant number 24NLTSZ003]。
文摘Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.
基金funded by Humanities and Social Sciences of Ministry of Education Planning Fund of China,grant number 21YJA790009National Natural Science Foundation of China,grant number 72140001.
文摘With the rapid proliferation of electric vehicles,their charging loads pose new challenges to power grid stability and operational efficiency.To address this,this study employs a Monte Carlo simulation model to analyze the charging load characteristics of six battery electric vehicle categories in Hebei Province,leveraging multi-source probabilistic distribution data under typical operational scenarios.The findings reveal that electric vehicle charging loads are primarily concentrated during midday and nighttime periods,with significant load fluctuations exerting substantial pressure on the grid.In response,this paper proposes strategic interventions including optimized charging infrastructure planning,time-of-use electricity pricing mechanisms,and smart charging technologies to balance grid loads.The results provide a theoretical foundation for electric vehicle load forecasting,smart grid dispatching,and vehicle-grid integration,thereby enhancing grid operational efficiency and sustainability.
基金funded by the Deanship of Scientific Research and Libraries at Princess Nourah bint Abdulrahman University,through the“Nafea”Program,Grant No.(NP-45-082).
文摘Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.
文摘Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-ities where operational records are scarce.This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such“cold-start”forecasting problems.It primarily covers three interrelated domains—solar photovoltaic(PV),wind power,and electrical load forecasting—where data scarcity and operational variability are most critical,while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective.To this end,we examined trends from over 150 predominantly peer-reviewed studies published between 2019 and mid-2025,highlighting advances in zero-shot and few-shot meta-learning frameworks that enable rapid model adaptation with minimal labeled data.Moreover,transfer learning approaches combined with spatiotemporal graph neural networks have been employed to transfer knowledge from existing energy assets to new,data-sparse environments,effectively capturing hidden dependencies among geographic features,meteorological dynamics,and grid structures.Synthetic data generation has further proven valuable for expanding training samples and mitigating overfitting in cold-start scenarios.In addition,large language models and explainable artificial intelligence(XAI)—notably conversational XAI systems—have been used to interpret and communicate complex model behaviors in accessible terms,fostering operator trust from the earliest deployment stages.By consolidating methodological advances,unresolved challenges,and open-source resources,this review provides a coherent overview of deep learning strategies that can shorten the data-sparse ramp-up period of new energy infrastructures and accelerate the transition toward resilient,low-carbon electricity grids.
基金supported by the National Natural Science Foundation of China(Grant Nos.42375062 and 42275158)the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab)the Natural Science Foundation of Gansu Province(Grant No.22JR5RF1080)。
文摘It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using an identical reference.In this study,three physically reasonable DLMs are implemented for the forecasting of the sea surface temperature(SST),sea level anomaly(SLA),and sea surface velocity in the South China Sea.The DLMs are validated against both the testing dataset and the“OceanPredict”Class 4 dataset.Results show that the DLMs'RMSEs against the latter increase by 44%,245%,302%,and 109%for SST,SLA,current speed,and direction,respectively,compared to those against the former.Therefore,different references have significant influences on the validation,and it is necessary to use an identical and independent reference to intercompare the DLMs and OFSs.Against the Class 4 dataset,the DLMs present significantly better performance for SLA than the OFSs,and slightly better performances for other variables.The error patterns of the DLMs and OFSs show a high degree of similarity,which is reasonable from the viewpoint of predictability,facilitating further applications of the DLMs.For extreme events,the DLMs and OFSs both present large but similar forecast errors for SLA and current speed,while the DLMs are likely to give larger errors for SST and current direction.This study provides an evaluation of the forecast skills of commonly used DLMs and provides an example to objectively intercompare different DLMs.
基金supported by National Natural Science Foundation of China(Grant Nos.42030605 and 42088101)National Key R&D Program of China(Grant No.2020YFA0608004)High Performance Computing of Nanjing University of Information Science&Technology for their support of this work。
文摘Marine heatwaves(MHWs),which can exert devastating socioeconomic and ecological impacts,have attracted much public interest in recent years.In this study,we evaluate the sub-seasonal forecast skill of MHWs based on the Nanjing University of Information Science&Technology Climate Forecast System version 1.1(NUIST CFS1.1)and analyze the related physical processes.Our results show that the model can accurately forecast the occurrence of MHWs on a global scale out to a lead time of 25 days.Notably,even at lead times of 51–55 days,the forecast skill in most tropical regions,as well as in the northeastern and southeastern Pacific,is superior to both random forecasts and persistence forecasts.Accurate predictions of sea level pressure,zonal currents,and mixed-layer depth are important for MHW forecasting.Furthermore,we also conduct forecast skill assessments for two well-documented MHW events.Due to its ability to correctly forecast the changes in heat flux anomalies at a lead time of 25 days,the model can accurately forecast the strong MHW event that occurred in the South China Sea in May–October 2020.However,the forecasting results were less than optimal for the strong MHW event that occurred along the Australian west coast in January–April 2011.Although the model accurately forecasts its occurrence,the forecast of its intensity is poor.Additionally,when the lead time exceeds 10 days,forecasts of the relevant physical processes of this MHW event are also inaccurate.
文摘The rapid development of technology has led to an ever-increasing demand for electrical energy.In the context of Timor-Leste,which still relies on fossil energy sources with high operational costs and significant environmental impacts,electricity load forecasting is a strategic measure to support the energy transition towards the Net Zero Emission(NZE)target by 2050.This study aims to utilize historical electricity load data for the period 2013–2024,as well as data on external factors affecting electricity consumption,to forecast electricity load in Timor-Leste in the next 10 years(2025–2035).The forecasting results are expected to support efforts in energy distribution efficiency,reduce operational costs,and inform decisions related to the sustainable energy transition.The method used in this study consists of two main approaches:the causality method,represented by the econometric Principal Component Analysis(PCA)model,which involves external factors in the data processing process,and the time series method,utilizing the LSTM,XGBoost,and hybrid(LSTM+XGBoost)models.In the time series method,data processing is combined with two approaches:the sliding window and the rolling recursive forecast.The performance of each model is evaluated using the Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Mean Absolute Percentage Error(MAPE).The model with the lowest MAPE(<10%)is considered the best-performing model,indicating the highest accuracy.Additionally,a Monte Carlo simulation with 50,000 iterations was used to process the data and measure the prediction uncertainty,as well as test the calibration of the electricity load projection data.The results showed that the hybrid model(LSTM+XGBoost)with a rolling forecast recursive approach is the best-performing model in predicting electricity load in Timor-Leste.This model yields an RMSE of 75.76 MW,an MAE of 55.76 MW,and an MAPE of 5.27%,indicating a high level of accuracy.In addition,the model is also indicated as one that fits the characteristics of electricity load in Timor-Leste,as it produces the lowest percentage of forecasting error in predicting electricity load.The integration of the best model with Monte Carlo Simulation,which yields a p-value of 0.565,suggests that the results of electricity load projections for the period 2025–2035 are well-calibrated,reliable,accurate,and unbiased.
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。
文摘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.
文摘In this study,we investigate the extent to which sustainability disclosures in the narrative sections of European banks’annual reports improve analysts’forecasting accuracy.We capture sustainability disclosures with a machine learning approach and use forecast errors as a proxy for analysts’forecast accuracy.Our results suggest that sustainability disclosures significantly improve analysts’forecasting accuracy by reducing forecast errors.In a further analysis,we also find that the introduction of Directive 2014/95/European Union is associated with increased disclosure content,which reduces forecast error.Collectively,our results suggest that sustainability disclosures improve forecast accuracy,and the introduction of the new EU directive strengthens this improvement.These results hold after several robustness tests.Our findings have important implications for market participants and policymakers.
文摘As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitations in handling nonlinear and high-dimensional data,while neural networks(NNs)have demonstrated great potential due to their powerful feature extraction and pattern recognition capabilities.Although several existing surveys discuss the applications of NNs in stock forecasting,they often lack a detailed examination of models that use time-series data as input and fail to cover the latest research developments.In response,this paper reviews relevant literature from 2015 to 2025 and classifies timeseriesbased stock forecasting methods into four categories:NNs,recurrent NNs(RNNs),convolutional NNs(CNNs),Transformers and other models.We analyze their performance under different market conditions,highlight strengths and limitations,and identify recent trends in model design.Our findings show that hybrid architectures and attention-based models consistently achieve superior forecasting stability and adaptability across volatile market scenarios.This survey offers a systematic reference for researchers and practitioners and outlines promising future research directions.
文摘The China Meteorological Administration(CMA)said that in the last five years,China has made big improvements in its weather services.This includes better weather forecasts and ways to protect people from disasters.
文摘According to the demand for weather forecast at the venues of the 14 th National Winter Games,based on the data of the fine grid model of the European Centre(EC)and RMAPS model,as well as the real-time observation data of the competition fields,a dynamic optimal correction method was proposed to improve the accuracy rate of temperature and wind speed prediction.Through techniques such as deviation correction and univariate linear regression,mathematical models applicable to different competition regions were constructed,and the effective correction of objective forecast products within 0-120 h were realized.The results show that this method significantly improved the accuracy rate of the prediction of temperature,wind speed and extreme wind speed,and the effect was more obvious especially when the model performance was unstable.Meanwhile,terrain and climate background had a significant impact on the correction effect.This study provides new technical support for mountain meteorological forecast.
基金supported by the National Natural Science Foundation of China(Grant Nos.42030610 and 42205006)the Startup Foundation for Introducing Talent of NUIST(2023r121)。
文摘This study focuses on an extreme rainfall event in East China during the mei-yu season,in which the capital city(Nanjing)of Jiangsu Province experienced a maximum 14-h rainfall accumulation of 209.6 mm and a peak hourly rainfall of 118.8 mm.The performance of two sets of convection-permitting ensemble forecast systems(CEFSs),each with 30 members and a 3-km horizontal grid spacing,is evaluated.The CEFS_ICBCs,using multiple initial and boundary conditions(ICs and BCs),and the CEFS_ICBCs Phys,which incorporates both multi-physics schemes and ICs/BCs,are compared to the CMA-REPS(China Meteorological Administration-Regional Ensemble Prediction System)with a coarser 10-km grid spacing.The two CEFSs demonstrate more uniform rank histograms and lower Brier scores(with higher resolution),improving precipitation intensity predictions and providing more reliable probability forecasts,although they overestimate precipitation over Mt.Dabie.It is challenging for the CEFSs to capture the evolution of mesoscale rainstorms that are known to be related to the errors in predicting the southwesterly low-level winds.Sensitivity experiments reveal that the microphysics and radiation schemes introduce considerable uncertainty in predicting the intensity and location of heavy rainfall in and near Nanjing and Mt.Dabie.In particular,the Asymmetric Convection Model 2(ACM2)planetary boundary layer scheme combined with the Pleim-Xiu surface layer scheme tends to produce a biased northeastward extension of the boundary-layer jet,contributing to the northeastward bias of heavy precipitation around Nanjing in the CEFS_ICBCs.
基金supported by the National Key Research and Development Program[grant number 2022YFC3004203]the S&T Development Fund of CAMS(Chinese Academy of Meteorological Sciences)[grant numbers 2023KJ040 and 2024KJ013].
文摘Subseasonal forecasting of extreme events is crucial for early warning systems.However,the forecast skills for extreme events are limited.Taking the extreme cold events in January 2018 as a specific example,and analyzing the 34 extreme cold events in East Asia from 1998 to 2020,the authors evaluated the forecast skills of the ECMWF model ensemble members on subseasonal time scales.The results show that while the ensemble mean has limited skills for forecasting extreme cold events at the 3-week lead time,some individual members demonstrate high forecast skills.For most extreme cold events,there are>10%of members among the total ensembles that can well predict the rapid temperature transitions at the 14-day lead time.This highlights the untapped potential of the ECMWF model to forecast extreme cold events on subseasonal time scales.High-skill ensemble members rely on accurate predictions of atmospheric circulation patterns(500-hPa geopotential height,mean sea level pressure)and key weather systems,including the Ural Blocking and Siberian High,that influence extreme cold events.
基金supported by the open foundation of State Key Laboratory of Chemical Engineering(SKL-ChE-22B01)the Natural Science Foundation of China(22008169).
文摘The dynamics of vapor−liquid−solid(V−L−S)flow boiling in fluidized bed evaporators exhibit inherent complexity and chaotic behavior,hindering accurate prediction of pressure drop signals.To address this challenge,this study proposes an innovative hybrid approach that integrates wavelet neural network(WNN)with chaos analysis.By leveraging the Cross-Correlation(C−C)method,the minimum embedding dimension for phase space reconstruction is systematically calculated and then adopted as the input node configuration for the WNN.Simulation results demonstrate the remarkable effectiveness of this integrated method in predicting pressure drop signals,advancing our understanding of the intricate dynamic phenomena occurring with V−L−S fluidized bed evaporators.Moreover,this study offers a novel perspective on applying advanced data-driven techniques to handle the complexities of multi-phase flow systems and highlights the potential for improved operational prediction and control in industrial settings.
基金supported by the funding from the Humanities and Social Science Fund of Ministry of Education of China(No.22YJCZH028)National Natural Science Foundation of China(Grant No.72303001)+3 种基金Fundamental Research Funds for the Central Universities(No.JUSRP124043)Anhui Provincial Excellent Young Scientists Fund for Universities(No.2024AH030001)Anhui Education Department Excellent Young Teachers Fund(No.YQYB2024021)Basic Research Program of Jiangsu(No.BK20251593)。
文摘The significance of accurately forecasting natural gas prices is far-reaching and significant,not only for the stable operation of the energy market,but also as a key element in promoting sustainable development and addressing environmental challenges.However,natural gas prices are affected by multiple source factors,presenting complex,unstable nonlinear characteristics hindering the improvement of the prediction accuracy of existing models.To address this issue,this study proposes an innovative multivariate combined forecasting model for natural gas prices.Initially,the study meticulously identifies and introduces 16 variables impacting natural gas prices across five crucial dimensions:the production,marketing,commodities,political and economic indicators of the United States and temperature.Subsequently,this study employs the least absolute shrinkage and selection operator,grey relation analysis,and random forest for dimensionality reduction,effectively screening out the most influential key variables to serve as input features for the subsequent learning model.Building upon this foundation,a suite of machine learning models is constructed to ensure precise natural gas price prediction.To further elevate the predictive performance,an intelligent algorithm for parameter optimization is incorporated,addressing potential limitations of individual models.To thoroughly assess the prediction accuracy of the proposed model,this study conducts three experiments using monthly natural gas trading prices.These experiments incorporate 19 benchmark models for comparative analysis,utilizing five evaluation metrics to quantify forecasting effectiveness.Furthermore,this study conducts in-depth validation of the proposed model's effectiveness through hypothesis testing,discussions on the improvement ratio of forecasting performance,and case studies on other energy prices.The empirical results demonstrate that the multivariate combined forecasting method developed in this study surpasses other comparative models in forecasting accuracy.It offers new perspectives and methodologies for natural gas price forecasting while also providing valuable insights for other energy price forecasting studies.
基金supported by the Startup Grant(PG18929)awarded to F.Shokoohi.
文摘Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environmental footprint by reducing the risks of disruption,downtime,and waste.However,with increasingly complex energy consumption patterns driven by renewable energy integration and changing consumer behaviors,no single approach has emerged as universally effective.In response,this research presents a hybrid modeling framework that combines the strengths of Random Forest(RF)and Autoregressive Integrated Moving Average(ARIMA)models,enhanced with advanced feature selection—Minimum Redundancy Maximum Relevancy and Maximum Synergy(MRMRMS)method—to produce a sparse model.Additionally,the residual patterns are analyzed to enhance forecast accuracy.High-resolution weather data from Weather Underground and historical energy consumption data from PJM for Duke Energy Ohio and Kentucky(DEO&K)are used in this application.This methodology,termed SP-RF-ARIMA,is evaluated against existing approaches;it demonstrates more than 40%reduction in mean absolute error and root mean square error compared to the second-best method.
