复杂地形风电场流动具有强烈的非定常现象和多尺度特征,其准确模拟是风资源精细化评估的难点。为兼顾宏观中尺度大气环流和微观非定常流动细节,该文结合中尺度气象研究与预报(weather research and forecasting,WRF)模式和微尺度计算流...复杂地形风电场流动具有强烈的非定常现象和多尺度特征,其准确模拟是风资源精细化评估的难点。为兼顾宏观中尺度大气环流和微观非定常流动细节,该文结合中尺度气象研究与预报(weather research and forecasting,WRF)模式和微尺度计算流体动力学(computational fluid dynamics,CFD)技术,构建一套WRF-CFD模式耦合的复杂地形风电场非定常仿真方法。以国际经典案例Askervein山和Bolund岛为验证对象,研究复杂地形流场中平均风速和湍流强度的分布特征,并简要分析复杂地形中风力机布置策略。结果表明,基于WRF-CFD模式的数值模拟结果与实验观测值有较好的一致性,且优于中尺度数值模拟结果,在选取的特征点位置,风速绝对误差均在2 m/s以内。结果可为风力机的设计、布局、载荷评估及风电场运行控制提供一定参考。展开更多
Symposium overview The International Symposium on Earthquake Forecasting to Commemorate the 50th Anniversary of the 1975 Haicheng M7.3 Earthquake,Liaoning,China,was held in Shenyang,China,from 8 to 11 July 2025.The sy...Symposium overview The International Symposium on Earthquake Forecasting to Commemorate the 50th Anniversary of the 1975 Haicheng M7.3 Earthquake,Liaoning,China,was held in Shenyang,China,from 8 to 11 July 2025.The symposium was organized by the Institute of Earthquake Forecasting,China Earthquake Administration(CEA),the State Key Laboratory of Earthquake Dynamics and Forecasting,and the China Seismic Experimental Site(CSES),in collaboration with the International Association of Seismology and Physics of the Earth’s Interior(IASPEI),the APEC Cooperation for Earthquake Science(ACES).展开更多
Since the initiation of the subseasonal-to-seasonal prediction project by the World Meteorological Organization,the accuracy of model forecasts has improved notably.However,substantial discrepancies have been observed...Since the initiation of the subseasonal-to-seasonal prediction project by the World Meteorological Organization,the accuracy of model forecasts has improved notably.However,substantial discrepancies have been observed among forecast results produced by different ensemble members when applied to South China.To enhance the accuracy of sub-seasonal forecasts in this region,it is essential to develop new methods that can effectively leverage multiple predictive models.This study introduces a weighted ensemble forecasting method based on online learning to improve forecast accuracy.We utilized ensemble forecasts from three models:the Integrated Forecasting System model from the European Centre for Medium-Range Weather Forecasts,the Climate Forecast System Version 2 model from the National Centers for Environmental Prediction,and the Beijing Climate Center-Climate Prediction System version 3 model from the China Meteorological Administration.The ensemble weights are trained using an online learning approach.The results indicate that the forecasts obtained through online learning outperform those of the original dynamical models.Compared to the simple ensemble results of the three models,the weighted ensemble model showed a stronger capability to capture temperature and precipitation patterns in South China.Therefore,this method has the potential to improve the accuracy of sub-seasonal forecasts in this region.展开更多
Wind speed is a crucial parameter affecting wind energy utilization.However,its volatility leads to time-varying power output.Herein,a novel Seq2Seq model integrating deep learning,data denoising,and a shape-aware los...Wind speed is a crucial parameter affecting wind energy utilization.However,its volatility leads to time-varying power output.Herein,a novel Seq2Seq model integrating deep learning,data denoising,and a shape-aware loss function is proposed for accurate multistep wind speed forecasting.In this model,the wind speed data is first denoised using the maximal overlap discrete wavelet transform.Next,an encoder-decoder network based on a temporal convolutional network,bidirectional gated recurrent unit,and multihead self-attention is employed for forecasting.Additionally,to enhance the ability of the model to identify temporal dynamics,a shape-aware loss function,ITILDE-Q,is employed in the model.To verify the effectiveness of the proposed model,a comparative experiment and an ablation experiment were conducted using three datasets of measured wind speeds.Three error metrics and a similarity metric were adopted for comprehensive evaluation.The experimental results showed that the proposed model consistently outperforms benchmark models in all tested forecasting scenarios,with particularly pronounced differences in performance over longer forecast horizons.Furthermore,the synergistic interaction of the three key components contributes to the extraordinary performance of the proposed model.展开更多
With the increasing penetration of variable renewable energy,flexible resources are highly needed to hedge the growing uncertainty,and variability in the power system.Demand response has served as a cost-effective typ...With the increasing penetration of variable renewable energy,flexible resources are highly needed to hedge the growing uncertainty,and variability in the power system.Demand response has served as a cost-effective type of flexible resource in recent years.In order to balance the uncertainty of the system,it is crucial to assess how much flexibility demand response programs can provide.Thus,forecasting demand response potential is important for the operation of the bulk system.This paper proposes a modeling approach that can characterize the multi-timescale flexibility of demand response so that not only the power potential but also temporal-coupling characteristics can be considered.Furthermore,a day-ahead demand response potential forecasting method is proposed using deep convolutional generative adversarial networks.The proposed forecasting method is tested using data from 170 users in Pecan Street Dataport.The results show that the proposed method can forecast the multi-timescale flexibility of demand response with high accuracy.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
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
Clouds are one of the leading causes of sun shading,which reduces the direct horizontal irradiance and curtails the photovoltaic(PV)power.It is critical to estimate cloud cover to accurately predict PV generation with...Clouds are one of the leading causes of sun shading,which reduces the direct horizontal irradiance and curtails the photovoltaic(PV)power.It is critical to estimate cloud cover to accurately predict PV generation within a very short horizon(second/minute).To achieve the precise forecasting of cloud cover,an image preprocessing method based on total-sky images is proposed to remove the interference and address the image edge distortion issue.An optimal threshold estimation method is further designed to achieve higher cloud identification precision.Considering the cloud's meteorological properties,a random hypersurface model(RHM)based on the Gaussian mixture probability hypothesis density(GM-PHD)filter is applied to track the cloud.The GM-PHD can track the rotation and diffusion of clouds,which helps to estimate sun-cloud collision.Furthermore,a hybrid autoregressive integrated moving average(ARIMA)and backpropagation(BP)neural network-based model is applied for intra-hour PV power forecasting.The experiment results demonstrate that the proposed cloud-tracking-based PV power forecasting model can capture the ramp behavior of PV power,improving forecasting precision.展开更多
Accurate forecasting of crude oil futures prices is crucial for understanding global energy market dynamics and formulating effective macroeconomic and energy strategies.However,the strong nonlinearity and multi-scale...Accurate forecasting of crude oil futures prices is crucial for understanding global energy market dynamics and formulating effective macroeconomic and energy strategies.However,the strong nonlinearity and multi-scale temporal characteristics of crude oil prices pose significant challenges to traditional forecasting methods.To address these issues,this study proposes a hybrid CEEMDAN–HOA–Transformer–GRU model that integrates decomposition,complexity analysis,adaptive modeling,and intelligent optimization.Specifically,Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)is employed to decompose the original series into multi-scale components,after which entropy-based complexity analysis quantitatively evaluates each component.A differentiated modeling strategy is then applied:Transformer networks capture long-term dependencies in high-complexity components,while Gated Recurrent Units(GRU)model short-term dynamics in relatively simple components.To further enhance robustness,the Hiking Optimization Algorithm(HOA)is used for joint hyperparameter optimization across both base learners.Empirical analysis of WTI and Brent crude oil futures demonstrates the technical effectiveness of the framework.Compared with benchmark models,the proposed method reduces RMSE by 79.16% for WTI and 77.47% for Brent.Incorporating complexity analysis further decreases RMSE by 36.51%for WTI and 34.93%for Brent,confirming the superior nonlinear modeling capacity and generalization performance of the integrated framework.Overall,this study provides not only a technically reliable tool for modeling complex financial time series but also practical guidance for improving the accuracy and stability of crude oil price forecasting,thereby supporting market monitoring,risk management,and policy formulation.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
The basic condition of earthquake disasters in China is featured by high frequency,strong intensity,wide distribution,and heavy losses.Earthquake forecasting plays a critical role in reducing seismic risks.To better a...The basic condition of earthquake disasters in China is featured by high frequency,strong intensity,wide distribution,and heavy losses.Earthquake forecasting plays a critical role in reducing seismic risks.To better advance earthquake predicting efforts,the China Earthquake Administration released the Strategic Plan for Earthquake Forecasting in China(2025−2035)on the occasion of the 50th anniversary of the Haicheng earthquake.Here we briefly introduce the main contents of the Strategic Plan,including the main progress,strategic objectives,and development directions of earthquake forecasting in China.展开更多
Multivariate time series forecasting plays a crucial role in decision-making for systems like energy grids and transportation networks,where temporal patterns emerge across diverse scales from short-term fluctuations ...Multivariate time series forecasting plays a crucial role in decision-making for systems like energy grids and transportation networks,where temporal patterns emerge across diverse scales from short-term fluctuations to long-term trends.However,existing Transformer-based methods often process data at a single resolution or handle multiple scales independently,overlooking critical cross-scale interactions that influence prediction accuracy.To address this gap,we introduce the Hierarchical Attention Transformer(HAT),which enables direct information exchange between temporal hierarchies through a novel cross-scale attention mechanism.HAT extracts multi-scale features using hierarchical convolutional-recurrent blocks,fuses them via temperature-controlled mechanisms,and optimizes gradient flow with residual connections for stable training.Evaluations on eight benchmark datasets show HAT outperforming state-of-the-art baselines,with average reductions of 8.2%in MSE and 7.5%in MAE across horizons,while achieving a 6.1×training speedup over patch-based methods.These advancements highlight HAT’s potential for applications requiring multi-resolution temporal modeling.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
The accuracy of photovoltaic(PV)power prediction is significantly influenced by meteorological and environmental factors.To enhance ultra-short-term forecasting precision,this paper proposes an interpretable feedback ...The accuracy of photovoltaic(PV)power prediction is significantly influenced by meteorological and environmental factors.To enhance ultra-short-term forecasting precision,this paper proposes an interpretable feedback prediction method based on a parallel dual-stream Temporal Convolutional Network-Bidirectional Long Short-Term Memory(TCN-BiLSTM)architecture incorporating a spatiotemporal attention mechanism.Firstly,during data preprocessing,the optimal historical time window is determined through autocorrelation analysis while highly correlated features are selected as model inputs using Pearson correlation coefficients.Subsequently,a parallel dual-stream TCN-BiLSTM model is constructed where the TCN branch extracts localized transient features and the BiLSTM branch captures long-term periodic patterns,with spatiotemporal attention dynamically weighting spatiotemporal dependencies.Finally,Shapley Additive explanations(SHAP)additive analysis quantifies feature contribution rates and provides optimization feedback to the model.Validation using operational data from a PV power station in Northeast China demonstrates that compared to conventional deep learning models,the proposed method achieves a 17.6%reduction in root mean square error(RMSE),a 5.4%decrease in training time consumption,and a 4.78%improvement in continuous ranked probability score(CRPS),exhibiting significant advantages in both prediction accuracy and generalization capability.This approach enhances the application effectiveness of ultra-short-term PV power forecasting while simultaneously improving prediction accuracy and computational efficiency.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
文摘复杂地形风电场流动具有强烈的非定常现象和多尺度特征,其准确模拟是风资源精细化评估的难点。为兼顾宏观中尺度大气环流和微观非定常流动细节,该文结合中尺度气象研究与预报(weather research and forecasting,WRF)模式和微尺度计算流体动力学(computational fluid dynamics,CFD)技术,构建一套WRF-CFD模式耦合的复杂地形风电场非定常仿真方法。以国际经典案例Askervein山和Bolund岛为验证对象,研究复杂地形流场中平均风速和湍流强度的分布特征,并简要分析复杂地形中风力机布置策略。结果表明,基于WRF-CFD模式的数值模拟结果与实验观测值有较好的一致性,且优于中尺度数值模拟结果,在选取的特征点位置,风速绝对误差均在2 m/s以内。结果可为风力机的设计、布局、载荷评估及风电场运行控制提供一定参考。
基金supported by the National Natural Science Foundation of China (No.U2039207)
文摘Symposium overview The International Symposium on Earthquake Forecasting to Commemorate the 50th Anniversary of the 1975 Haicheng M7.3 Earthquake,Liaoning,China,was held in Shenyang,China,from 8 to 11 July 2025.The symposium was organized by the Institute of Earthquake Forecasting,China Earthquake Administration(CEA),the State Key Laboratory of Earthquake Dynamics and Forecasting,and the China Seismic Experimental Site(CSES),in collaboration with the International Association of Seismology and Physics of the Earth’s Interior(IASPEI),the APEC Cooperation for Earthquake Science(ACES).
基金Science and Technology Development Program of the“Taihu Light”(K20231023)CMA Numerical Weather Prediction R&D Project(TCYF2024QH007)+1 种基金“Qing Lan”Project of Jiangsu Province for C.H.LUWuxi University Research Start-up Fund for Introduced Talents(2023r037)。
文摘Since the initiation of the subseasonal-to-seasonal prediction project by the World Meteorological Organization,the accuracy of model forecasts has improved notably.However,substantial discrepancies have been observed among forecast results produced by different ensemble members when applied to South China.To enhance the accuracy of sub-seasonal forecasts in this region,it is essential to develop new methods that can effectively leverage multiple predictive models.This study introduces a weighted ensemble forecasting method based on online learning to improve forecast accuracy.We utilized ensemble forecasts from three models:the Integrated Forecasting System model from the European Centre for Medium-Range Weather Forecasts,the Climate Forecast System Version 2 model from the National Centers for Environmental Prediction,and the Beijing Climate Center-Climate Prediction System version 3 model from the China Meteorological Administration.The ensemble weights are trained using an online learning approach.The results indicate that the forecasts obtained through online learning outperform those of the original dynamical models.Compared to the simple ensemble results of the three models,the weighted ensemble model showed a stronger capability to capture temperature and precipitation patterns in South China.Therefore,this method has the potential to improve the accuracy of sub-seasonal forecasts in this region.
基金supported by the National Natural Science Foundation of China(No.52171284)。
文摘Wind speed is a crucial parameter affecting wind energy utilization.However,its volatility leads to time-varying power output.Herein,a novel Seq2Seq model integrating deep learning,data denoising,and a shape-aware loss function is proposed for accurate multistep wind speed forecasting.In this model,the wind speed data is first denoised using the maximal overlap discrete wavelet transform.Next,an encoder-decoder network based on a temporal convolutional network,bidirectional gated recurrent unit,and multihead self-attention is employed for forecasting.Additionally,to enhance the ability of the model to identify temporal dynamics,a shape-aware loss function,ITILDE-Q,is employed in the model.To verify the effectiveness of the proposed model,a comparative experiment and an ablation experiment were conducted using three datasets of measured wind speeds.Three error metrics and a similarity metric were adopted for comprehensive evaluation.The experimental results showed that the proposed model consistently outperforms benchmark models in all tested forecasting scenarios,with particularly pronounced differences in performance over longer forecast horizons.Furthermore,the synergistic interaction of the three key components contributes to the extraordinary performance of the proposed model.
基金supported by the National Key R&D Program of China(No.2021YFB2401200)National Natural Science Foundation of China(72242105)Organized Research Support Program,Department of Electrical Engineering,Tsinghua University.
文摘With the increasing penetration of variable renewable energy,flexible resources are highly needed to hedge the growing uncertainty,and variability in the power system.Demand response has served as a cost-effective type of flexible resource in recent years.In order to balance the uncertainty of the system,it is crucial to assess how much flexibility demand response programs can provide.Thus,forecasting demand response potential is important for the operation of the bulk system.This paper proposes a modeling approach that can characterize the multi-timescale flexibility of demand response so that not only the power potential but also temporal-coupling characteristics can be considered.Furthermore,a day-ahead demand response potential forecasting method is proposed using deep convolutional generative adversarial networks.The proposed forecasting method is tested using data from 170 users in Pecan Street Dataport.The results show that the proposed method can forecast the multi-timescale flexibility of demand response with high accuracy.
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supported by National Natural Science Foundation of China(U1909201,62206062).
文摘Clouds are one of the leading causes of sun shading,which reduces the direct horizontal irradiance and curtails the photovoltaic(PV)power.It is critical to estimate cloud cover to accurately predict PV generation within a very short horizon(second/minute).To achieve the precise forecasting of cloud cover,an image preprocessing method based on total-sky images is proposed to remove the interference and address the image edge distortion issue.An optimal threshold estimation method is further designed to achieve higher cloud identification precision.Considering the cloud's meteorological properties,a random hypersurface model(RHM)based on the Gaussian mixture probability hypothesis density(GM-PHD)filter is applied to track the cloud.The GM-PHD can track the rotation and diffusion of clouds,which helps to estimate sun-cloud collision.Furthermore,a hybrid autoregressive integrated moving average(ARIMA)and backpropagation(BP)neural network-based model is applied for intra-hour PV power forecasting.The experiment results demonstrate that the proposed cloud-tracking-based PV power forecasting model can capture the ramp behavior of PV power,improving forecasting precision.
基金funded by the Henan Provincial Natural Science Foundation(grant no.242300421257).
文摘Accurate forecasting of crude oil futures prices is crucial for understanding global energy market dynamics and formulating effective macroeconomic and energy strategies.However,the strong nonlinearity and multi-scale temporal characteristics of crude oil prices pose significant challenges to traditional forecasting methods.To address these issues,this study proposes a hybrid CEEMDAN–HOA–Transformer–GRU model that integrates decomposition,complexity analysis,adaptive modeling,and intelligent optimization.Specifically,Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)is employed to decompose the original series into multi-scale components,after which entropy-based complexity analysis quantitatively evaluates each component.A differentiated modeling strategy is then applied:Transformer networks capture long-term dependencies in high-complexity components,while Gated Recurrent Units(GRU)model short-term dynamics in relatively simple components.To further enhance robustness,the Hiking Optimization Algorithm(HOA)is used for joint hyperparameter optimization across both base learners.Empirical analysis of WTI and Brent crude oil futures demonstrates the technical effectiveness of the framework.Compared with benchmark models,the proposed method reduces RMSE by 79.16% for WTI and 77.47% for Brent.Incorporating complexity analysis further decreases RMSE by 36.51%for WTI and 34.93%for Brent,confirming the superior nonlinear modeling capacity and generalization performance of the integrated framework.Overall,this study provides not only a technically reliable tool for modeling complex financial time series but also practical guidance for improving the accuracy and stability of crude oil price forecasting,thereby supporting market monitoring,risk management,and policy formulation.
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.
文摘The basic condition of earthquake disasters in China is featured by high frequency,strong intensity,wide distribution,and heavy losses.Earthquake forecasting plays a critical role in reducing seismic risks.To better advance earthquake predicting efforts,the China Earthquake Administration released the Strategic Plan for Earthquake Forecasting in China(2025−2035)on the occasion of the 50th anniversary of the Haicheng earthquake.Here we briefly introduce the main contents of the Strategic Plan,including the main progress,strategic objectives,and development directions of earthquake forecasting in China.
文摘Multivariate time series forecasting plays a crucial role in decision-making for systems like energy grids and transportation networks,where temporal patterns emerge across diverse scales from short-term fluctuations to long-term trends.However,existing Transformer-based methods often process data at a single resolution or handle multiple scales independently,overlooking critical cross-scale interactions that influence prediction accuracy.To address this gap,we introduce the Hierarchical Attention Transformer(HAT),which enables direct information exchange between temporal hierarchies through a novel cross-scale attention mechanism.HAT extracts multi-scale features using hierarchical convolutional-recurrent blocks,fuses them via temperature-controlled mechanisms,and optimizes gradient flow with residual connections for stable training.Evaluations on eight benchmark datasets show HAT outperforming state-of-the-art baselines,with average reductions of 8.2%in MSE and 7.5%in MAE across horizons,while achieving a 6.1×training speedup over patch-based methods.These advancements highlight HAT’s potential for applications requiring multi-resolution temporal modeling.
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.
基金funded by the National Natural Science Foundation of China(NSFC)(No.62066024)funded by Basic Scientific Research Projects of Higher Education Institutions in Liaoning Province(LJ212411632063)the National Undergraduate Training Program for Innovation and Entrepreneurship(S202511632045).
文摘The accuracy of photovoltaic(PV)power prediction is significantly influenced by meteorological and environmental factors.To enhance ultra-short-term forecasting precision,this paper proposes an interpretable feedback prediction method based on a parallel dual-stream Temporal Convolutional Network-Bidirectional Long Short-Term Memory(TCN-BiLSTM)architecture incorporating a spatiotemporal attention mechanism.Firstly,during data preprocessing,the optimal historical time window is determined through autocorrelation analysis while highly correlated features are selected as model inputs using Pearson correlation coefficients.Subsequently,a parallel dual-stream TCN-BiLSTM model is constructed where the TCN branch extracts localized transient features and the BiLSTM branch captures long-term periodic patterns,with spatiotemporal attention dynamically weighting spatiotemporal dependencies.Finally,Shapley Additive explanations(SHAP)additive analysis quantifies feature contribution rates and provides optimization feedback to the model.Validation using operational data from a PV power station in Northeast China demonstrates that compared to conventional deep learning models,the proposed method achieves a 17.6%reduction in root mean square error(RMSE),a 5.4%decrease in training time consumption,and a 4.78%improvement in continuous ranked probability score(CRPS),exhibiting significant advantages in both prediction accuracy and generalization capability.This approach enhances the application effectiveness of ultra-short-term PV power forecasting while simultaneously improving prediction accuracy and computational efficiency.
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.
