For short-term PV power prediction,based on interval type-2 Takagi-Sugeno-Kang fuzzy logic systems(IT2 TSK FLS),combined with improved grey wolf optimizer(IGWO)algorithm,an IGWO-IT2 TSK FLS method was proposed.Compare...For short-term PV power prediction,based on interval type-2 Takagi-Sugeno-Kang fuzzy logic systems(IT2 TSK FLS),combined with improved grey wolf optimizer(IGWO)algorithm,an IGWO-IT2 TSK FLS method was proposed.Compared with the type-1 TSK fuzzy logic system method,interval type-2 fuzzy sets could simultaneously model both intra-personal uncertainty and inter-personal uncertainty based on the training of the existing error back propagation(BP)algorithm,and the IGWO algorithm was used for training the model premise and consequent parameters to further improve the predictive performance of the model.By improving the gray wolf optimization algorithm,the early convergence judgment mechanism,nonlinear cosine adjustment strategy,and Levy flight strategy were introduced to improve the convergence speed of the algorithm and avoid the problem of falling into local optimum.The interval type-2 TSK FLS method based on the IGWO algorithm was applied to the real-world photovoltaic power time series forecasting instance.Under the same conditions,it was also compared with different IT2 TSK FLS methods,such as type I TSK FLS method,BP algorithm,genetic algorithm,differential evolution,particle swarm optimization,biogeography optimization,gray wolf optimization,etc.Experimental results showed that the proposed method based on IGWO algorithm outperformed other methods in performance,showing its effectiveness and application potential.展开更多
Predicting player performance in sports is a critical challenge with significant implications for team success,fan engagement,and financial outcomes.Although,inMajor League Baseball(MLB),statistical methodologies such...Predicting player performance in sports is a critical challenge with significant implications for team success,fan engagement,and financial outcomes.Although,inMajor League Baseball(MLB),statistical methodologies such as sabermetrics have been widely used,the dynamic nature of sports makes accurate performance prediction a difficult task.Enhanced forecasts can provide immense value to team managers by aiding strategic player contract and acquisition decisions.This study addresses this challenge by employing the temporal fusion transformer(TFT),an advanced and cutting-edge deep learning model for complex data,to predict pitchers’earned run average(ERA),a key metric in baseball performance analysis.The performance of the TFT model is evaluated against recurrent neural network-based approaches and existing projection systems.In experimental results,the TFT based model consistently outperformed its counterparts,demonstrating superior accuracy in pitcher performance prediction.By leveraging the advanced capabilities of TFT,this study contributes to more precise player evaluations and improves strategic planning in baseball.展开更多
With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the lit...With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the literature revealed that most studies tend to use several performance criteria to evaluate the performance of competing forecasting models;however, models are compared to each other using a single criterion at a time, which often leads to different rankings for different criteria—A situation where one cannot make an informed decision as to which model performs best when taking all criteria into account. In order to overcome this methodological problem, Xu and Ouenniche [1] proposed a multidimensional framework based on an input-oriented radial super-efficiency Data Envelopment Analysis (DEA) model to rank order competing forecasting models of crude oil prices’ volatility. However, their approach suffers from a number of issues. In this paper, we overcome such issues by proposing an alternative framework.展开更多
In this paper, the authors develop the earlier work of Chen Jiabin et al. (1986). In order to reduce spectral truncation errors, the reference atmosphere has been introduced in ECMWF model, and the spectrally-represen...In this paper, the authors develop the earlier work of Chen Jiabin et al. (1986). In order to reduce spectral truncation errors, the reference atmosphere has been introduced in ECMWF model, and the spectrally-represented variables, temperature, geopotential height and orography, are replaced by their deviations from the reference atmosphere. Two modified semi- implicit schemes have been proposed to alleviate the computational instability due to the introduction of reference atmosphere. Concerning the deviation of surface geopotential height from reference atmosphere, an exact computational formulation has been used instead of the approximate one in the earlier work. To re duce aliasing errors in the computations of the deviation of the surface geopotential height, a spectral fit has been used slightly to modify the original Gaussian grid-point values of orography.A series of experiments has been performed in order to assess the impact of the reference atmosphere on ECMWF medium- range forecasts at the resolution T21, T42 and T63. The results we have obtained reveal that the reference atmosphere introduced in ECMWF spectral model is generally beneficial to the mean statistical scores of 1000-200 hPa height 10-day forecasts over the globe. In the Southern Hemisphere, it is a clear improvement for T21, T42 and T63 throughout the 10-day forecast period. In the Northern Hemisphere, the impact of the reference atmos phere on anomaly correlation is positive for resolution T21, a very slightly damaging at T42 and almost neutral at T63 in the range of day 1 to day 4. Beyond the day 4 there is a clear improvement at all resolutions.展开更多
A system designed for supporting the network performance analysis and forecast effort is presented, based on the combination of offline network analysis and online real-time performance forecast. The off-line analysis...A system designed for supporting the network performance analysis and forecast effort is presented, based on the combination of offline network analysis and online real-time performance forecast. The off-line analysis will perform analysis of specific network node performance, correlation analysis of relative network nodes performance and evolutionary mathematical modeling of long-term network performance measurements. The online real-time network performance forecast will be based on one so-called hybrid prediction modeling approach for short-term network, performance prediction and trend analysis. Based on the module design, the system proposed has good intelligence, scalability and self-adaptability, which will offer highly effective network performance analysis and forecast tools for network managers, and is one ideal support platform for network performance analysis and forecast effort.展开更多
Meiyu,featuring prolonged periods of rainfall over the Yangtze-Huai River basin(YHRB),not only replenishes water resources and sustains ecological balance,but also poses potential disaster risks.Accurate early forecas...Meiyu,featuring prolonged periods of rainfall over the Yangtze-Huai River basin(YHRB),not only replenishes water resources and sustains ecological balance,but also poses potential disaster risks.Accurate early forecasting of Meiyu is crucial for effectively implementing flood prevention strategies.To help refine numerical models and providing guidance for operational forecasters,this study explores the capabilities of two global ensemble prediction systems(GEPSs)of the China Meteorological Administration(CMA)and the ECMWF in forecasting the Meiyu characteristics in 2023 over the YHRB.Results show that the ECMWF GEPS reasonably forecasts the Meiyu rainfall,while the CMA GEPS presents a notable underestimation.The predictable lead time of the Meiyu onset date is eight days by the ECMWF GEPS and six days by the CMA GEPS,respectively.Regarding the regional rainstorm processes,the two GEPSs generally provide a predictable lead time of 48-168 h for reasonably forecasting the patterns of the heavy rainfall area.To further examine their strengths and weaknesses in Meiyu forecasting,this paper revisits their abilities in forecasting key influence systems.By verifying against their respective analyses,it is demonstrated that the ECMWF GEPS reasonably forecasts the spatial coverages of the northwestern Pacific subtropical high(NWPSH)and the South Asian high(SAH),whereas the CMA GEPS presents substantial underestimation.Both GEPSs show generally southward deviations for the eastern ridge line position(RLP)of the SAH,and exhibit a northward deviation for the western RLP of the NWPSH during early forecast lead times.The less Meiyu rainfall predicted by the CMA GEPS compared to the ECMWF GEPS can be attributed to its weaker low-level convergence belt and weaker upper-level divergence area.A deeper exploration into these forecast discrepancies in upper-level divergence and lower-level convergence suggests that they likely originate from their initial analysis fields.展开更多
Implementing a CO2 flooding scheme successfully requires the capacity to get accurate information of reservoir dynamic performance and fluids injected. Despite some numerical simulation studies, the complicated drive ...Implementing a CO2 flooding scheme successfully requires the capacity to get accurate information of reservoir dynamic performance and fluids injected. Despite some numerical simulation studies, the complicated drive mechanisms and actual reservoir performance have not been fully understood. There is a strong need to develop models from different perspectives to complement current simulators and provide valuable insights into the reservoir performance during CO2 flooding. The aim of this study is to develop a model by using an improved material balance equation （MBE） to analyze quickly the performance of CO2 flooding. After matching the historical field data the proposed model can be used to evaluate, monitor and predict the overall reservoir dynamic performance during CO2 flooding. In order to account accurately for the complex displacement process involving compositional effect and multiphase flow, the PVT properties and flowability of reservoir fluids are incorporated in the model. This study investigates the effects of a number of factors, such as reservoir pressure, the amount of CO2 injected, the CO2 partition ratios in reservoir fluids, the possibility of the existence of a free CO2 gas cap, the proportion of reservoir fluids contacted with CO2, the starting time of CO2 flooding, oil swelling, and oil flowability improvement by mixing with CO2. The model was used to analyze the CO2 flooding project in Weyburn oil field, Saskatchewan, Canada. This study shows that the proposed model is an effective complementary tool to analyze and monitor the overall reservoir performance during CO2 flooding.展开更多
Traffic forecasting provides the estimation of future traffic state to help traffic control,travel guide,etc. This paper compared several widely used traffic forecasting methods,and analyzed each one's performance...Traffic forecasting provides the estimation of future traffic state to help traffic control,travel guide,etc. This paper compared several widely used traffic forecasting methods,and analyzed each one's performance in detail to make conclusions,which could redound to researchers choosing an appropriate traffic forecasting method in their own works. Compared with conventional works,this paper creatively assessed the performance of traffic forecasting methods based on travel time index (TTI) data prediction,which made the accuracy of our comparison better.展开更多
As after sales services become more and more popular,particularly preventive or corrective maintenance,the intervention and repair of the customer’s goods in a timely and efficient manner ensure customer ...As after sales services become more and more popular,particularly preventive or corrective maintenance,the intervention and repair of the customer’s goods in a timely and efficient manner ensure customer satisfaction and contribute to the establishment of brand image in the market of the suppliers.The availability and quality of spare parts are key elements of this strategy while ensuring minimal management costs.The reuse of spare parts retrieved from customer systems is a growing maintenance strategy practice which impacts the traditional spare parts supply chain.This reuse is primarily driven by extending the economic life of goods,initially regarded as waste and therefore without added value,by transforming them into valuable spare parts that can be reused;secondly,for environmental or regulatory reasons,demanding responsibility for the treatment of products at the end of their life;and thirdly,to improve the availability of parts for maintenance,especially parts that the organization can no longer purchase or that are impacted by other issues.It also involves the analysis of their condition and their eventual return to working order as they are retrieved from the customer’s systems in a defective condition.In this paper,we will identify and classify the different customers and spare parts by estimating the critical level of rationing policy based on forecasts,identify the thresholds of inventory management policies,and finally,compare the different policies by service level and inventory level performance for the N.A.C.C.company.展开更多
In this study,we address a demanding time series forecasting problem that deals simultaneously with the following:(1)intermittent time series,(2)multi-step ahead forecasting,(3)time series with multiple seasonal perio...In this study,we address a demanding time series forecasting problem that deals simultaneously with the following:(1)intermittent time series,(2)multi-step ahead forecasting,(3)time series with multiple seasonal periods,and(4)performance measures for model selection across multiple time series.Current literature deals with these types of problems separately,and no study has dealt with all these characteristics simultaneously.To fill this knowledge gap,we begin by reviewing all the necessary existing literature relevant to this case study with the goal of proposing a framework capable of achieving adequate forecast accuracy for such a complex problem.Several adaptions and innovations have been conducted,which are marked as contributions to the literature.Specifically,we proposed a weighted average forecast combination of many cutting-edge models based on their out-of-sample performance.To gather strong evidence that our ensemble model works in practice,we undertook a large-scale study across 98 time series,rigorously assessed with unbiased performance measures,where a week seasonal naïve was set as a benchmark.The results demonstrate that the proposed ensemble model achieves eyecatching forecasting accuracy.展开更多
Accurate pavement performance prediction plays a critical role in formulating maintenance and repair strategies for transportation departments,enabling the achievement of better pavement performance with limited finan...Accurate pavement performance prediction plays a critical role in formulating maintenance and repair strategies for transportation departments,enabling the achievement of better pavement performance with limited financial resources.However,due to the intricate influence of numerous factors on pavement performance deterioration,improving the accuracy of pavement performance prediction poses a challenge for conventional models.Therefore,the aim of this study is to establish a machine learning-based pavement performance prediction model.First,this study considers five factors that affect pavement performance,including pavement initial performance indicators,traffic loads,weather,pavement structure,and maintenance measures,and identifies 15 specific indicators that affect pavement performance based on these five factors.Then,based on the the long-term pavement performance(LTPP)database,the study screens and summarizes these indicators,obtaining 2464 high-quality pavement performance data for pavement conditions index(PCI)prediction and 3238 high-quality pavement performance data for international roughness index(IRI)prediction.Finally,three distinct prediction models are established,namely,the fully connected neural network(FCNN)model,the long short-term memory(LSTM)model,and the combined LSTM-attention model.The study shows that the LSTM-attention model performs significantly better than the FCNN and LSTM models,with an R2 coefficient of determination of 0.81 for PCI and 0.79 for IRI.The innovation of this paper is that the authors have introduced the attention mechanism on the basic of the LSTM model,which makes the fitting accuracy of the prediction model further improved.展开更多
Due to the low dispatchability of wind power,the massive integration of this energy source in power systems requires short-term and very short-term wind power output forecasting models to be as efficient and stable as...Due to the low dispatchability of wind power,the massive integration of this energy source in power systems requires short-term and very short-term wind power output forecasting models to be as efficient and stable as possible.A study is conducted in the present paper of potential improvements to the performance of artificial neural network(ANN)models in terms of efficiency and stability.Generally,current ANN models have been developed by considering exclusively the meteorological information of the wind farm reference station,in addition to selecting a fixed number of time periods prior to the forecasting.In this respect,new ANN models are proposed in this paper,which are developed by:varying the number of prior 1-h periods(periods prior to the forecasting hour)chosen for the input layer parameters;and/or incorporating in the input layer data from a second weather station in addition to the wind farm reference station.It has been found that the model performance is always improved when data from a second weather station are incorporated.The mean absolute relative error(MARE)of the new models is reduced by up to 7.5%.Furthermore,the longer the forecasting horizon,the greater the degree of improvement.展开更多
This paper presents a new index system for the performance evaluation and network planning of multimedia communication systems using measurement on actual systems to support several different traffic types. In this in...This paper presents a new index system for the performance evaluation and network planning of multimedia communication systems using measurement on actual systems to support several different traffic types. In this index system, we develop an expert system to evaluate the performance of such multimedia communication networks including channel utilization and call blocking probability and packet delay, and apply the network planning methods to optimize the networks and forecast the demand of the growing multimedia communications systems. Two important planning problems for the multimedia communication systems are presented: optimization problem for construction of the world system and forecast problem for increasing traffic demands. We first discuss analysis methods, performance measures for the multimedia communication systems. Then, we describe network planning methods for the multimedia communication systems and give some efficiency network planning methods. Finally, we present some results studied in traffic forecast for the campus network and show the effectiveness of these methods.展开更多
In recent work,three physical factors of the Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation(DSAEF_LTP model)have been introduced,namely,tropical cyclone(TC)track,TC landfall...In recent work,three physical factors of the Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation(DSAEF_LTP model)have been introduced,namely,tropical cyclone(TC)track,TC landfall season,and TC intensity.In the present study,we set out to test the forecasting performance of the improved model with new similarity regions and ensemble forecast schemes added.Four experiments associated with the prediction of accumulated precipitation were conducted based on 47 landfalling TCs that occurred over South China during 2004-2018.The first experiment was designed as the DSAEF_LTP model with TC track,TC landfall season,and intensity(DSAEF_LTP-1).The other three experiments were based on the first experiment,but with new ensemble forecast schemes added(DSAEF_LTP-2),new similarity regions added(DSAEF_LTP-3),and both added(DSAEF_LTP-4),respectively.Results showed that,after new similarity regions added into the model(DSAEF_LTP-3),the forecasting performance of the DSAEF_LTP model for heavy rainfall(accumulated precipitation≥250 mm and≥100 mm)improved,and the sum of the threat score(TS250+TS100)increased by 4.44%.Although the forecasting performance of DSAEF_LTP-2 was the same as that of DSAEF_LTP-1,the forecasting performance was significantly improved and better than that of DSAEF_LTP-3 when the new ensemble schemes and similarity regions were added simultaneously(DSAEF_LTP-4),with the TS increasing by 25.36%.Moreover,the forecasting performance of the four experiments was compared with four operational numerical weather prediction models,and the comparison indicated that the DSAEF_LTP model showed advantages in predicting heavy rainfall.Finally,some issues associated with the experimental results and future improvements of the DSAEF_LTP model were discussed.展开更多
Because radiation belt electrons can pose a potential threat to the safety of satellites orbiting in space,it is of great importance to develop a reliable model that can predict the highly dynamic variations in outer ...Because radiation belt electrons can pose a potential threat to the safety of satellites orbiting in space,it is of great importance to develop a reliable model that can predict the highly dynamic variations in outer radiation belt electron fluxes.In the present study,we develop a forecast model of radiation belt electron fluxes based on the data assimilation method,in terms of Van Allen Probe measurements combined with three-dimensional radiation belt numerical simulations.Our forecast model can cover the entire outer radiation belt with a high temporal resolution(1 hour)and a spatial resolution of 0.25 L over a wide range of both electron energy(0.1-5.0 MeV)and pitch angle(5°-90°).On the basis of this model,we forecast hourly electron fluxes for the next 1,2,and 3 days during an intense geomagnetic storm and evaluate the corresponding prediction performance.Our model can reasonably predict the stormtime evolution of radiation belt electrons with high prediction efficiency(up to~0.8-1).The best prediction performance is found for~0.3-3 MeV electrons at L=~3.25-4.5,which extends to higher L and lower energies with increasing pitch angle.Our results demonstrate that the forecast model developed can be a powerful tool to predict the spatiotemporal changes in outer radiation belt electron fluxes,and the model has both scientific significance and practical implications.展开更多
基金supported by National Natural Science Foundation of China(No.12172157)Key Project of Natural Science Foundation of Gansu Province(No.25JRRA150)Key Research and Development Planning Project of Gansu Province(No.23YFWA0007).
文摘For short-term PV power prediction,based on interval type-2 Takagi-Sugeno-Kang fuzzy logic systems(IT2 TSK FLS),combined with improved grey wolf optimizer(IGWO)algorithm,an IGWO-IT2 TSK FLS method was proposed.Compared with the type-1 TSK fuzzy logic system method,interval type-2 fuzzy sets could simultaneously model both intra-personal uncertainty and inter-personal uncertainty based on the training of the existing error back propagation(BP)algorithm,and the IGWO algorithm was used for training the model premise and consequent parameters to further improve the predictive performance of the model.By improving the gray wolf optimization algorithm,the early convergence judgment mechanism,nonlinear cosine adjustment strategy,and Levy flight strategy were introduced to improve the convergence speed of the algorithm and avoid the problem of falling into local optimum.The interval type-2 TSK FLS method based on the IGWO algorithm was applied to the real-world photovoltaic power time series forecasting instance.Under the same conditions,it was also compared with different IT2 TSK FLS methods,such as type I TSK FLS method,BP algorithm,genetic algorithm,differential evolution,particle swarm optimization,biogeography optimization,gray wolf optimization,etc.Experimental results showed that the proposed method based on IGWO algorithm outperformed other methods in performance,showing its effectiveness and application potential.
基金supported by SKKU Global Research Platform Research Fund,Sungkyunkwan University,2024-2025.
文摘Predicting player performance in sports is a critical challenge with significant implications for team success,fan engagement,and financial outcomes.Although,inMajor League Baseball(MLB),statistical methodologies such as sabermetrics have been widely used,the dynamic nature of sports makes accurate performance prediction a difficult task.Enhanced forecasts can provide immense value to team managers by aiding strategic player contract and acquisition decisions.This study addresses this challenge by employing the temporal fusion transformer(TFT),an advanced and cutting-edge deep learning model for complex data,to predict pitchers’earned run average(ERA),a key metric in baseball performance analysis.The performance of the TFT model is evaluated against recurrent neural network-based approaches and existing projection systems.In experimental results,the TFT based model consistently outperformed its counterparts,demonstrating superior accuracy in pitcher performance prediction.By leveraging the advanced capabilities of TFT,this study contributes to more precise player evaluations and improves strategic planning in baseball.
文摘With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the literature revealed that most studies tend to use several performance criteria to evaluate the performance of competing forecasting models;however, models are compared to each other using a single criterion at a time, which often leads to different rankings for different criteria—A situation where one cannot make an informed decision as to which model performs best when taking all criteria into account. In order to overcome this methodological problem, Xu and Ouenniche [1] proposed a multidimensional framework based on an input-oriented radial super-efficiency Data Envelopment Analysis (DEA) model to rank order competing forecasting models of crude oil prices’ volatility. However, their approach suffers from a number of issues. In this paper, we overcome such issues by proposing an alternative framework.
文摘In this paper, the authors develop the earlier work of Chen Jiabin et al. (1986). In order to reduce spectral truncation errors, the reference atmosphere has been introduced in ECMWF model, and the spectrally-represented variables, temperature, geopotential height and orography, are replaced by their deviations from the reference atmosphere. Two modified semi- implicit schemes have been proposed to alleviate the computational instability due to the introduction of reference atmosphere. Concerning the deviation of surface geopotential height from reference atmosphere, an exact computational formulation has been used instead of the approximate one in the earlier work. To re duce aliasing errors in the computations of the deviation of the surface geopotential height, a spectral fit has been used slightly to modify the original Gaussian grid-point values of orography.A series of experiments has been performed in order to assess the impact of the reference atmosphere on ECMWF medium- range forecasts at the resolution T21, T42 and T63. The results we have obtained reveal that the reference atmosphere introduced in ECMWF spectral model is generally beneficial to the mean statistical scores of 1000-200 hPa height 10-day forecasts over the globe. In the Southern Hemisphere, it is a clear improvement for T21, T42 and T63 throughout the 10-day forecast period. In the Northern Hemisphere, the impact of the reference atmos phere on anomaly correlation is positive for resolution T21, a very slightly damaging at T42 and almost neutral at T63 in the range of day 1 to day 4. Beyond the day 4 there is a clear improvement at all resolutions.
基金the National 863 High-Tech Project (863 -3 0 0 -0 2 -0 9-99) and Key Research Project of Hubei Province(991P110 )
文摘A system designed for supporting the network performance analysis and forecast effort is presented, based on the combination of offline network analysis and online real-time performance forecast. The off-line analysis will perform analysis of specific network node performance, correlation analysis of relative network nodes performance and evolutionary mathematical modeling of long-term network performance measurements. The online real-time network performance forecast will be based on one so-called hybrid prediction modeling approach for short-term network, performance prediction and trend analysis. Based on the module design, the system proposed has good intelligence, scalability and self-adaptability, which will offer highly effective network performance analysis and forecast tools for network managers, and is one ideal support platform for network performance analysis and forecast effort.
基金Supported by the National Natural Science Foundation of China(U2142205)CMA Innovative Development Project(CXFZ2022J009)+1 种基金Key Laboratory of Hydro-Meteorology CMA Research Projects(23SWQXM030)National Key Research and Development Program of China(2018YFC1507703).
文摘Meiyu,featuring prolonged periods of rainfall over the Yangtze-Huai River basin(YHRB),not only replenishes water resources and sustains ecological balance,but also poses potential disaster risks.Accurate early forecasting of Meiyu is crucial for effectively implementing flood prevention strategies.To help refine numerical models and providing guidance for operational forecasters,this study explores the capabilities of two global ensemble prediction systems(GEPSs)of the China Meteorological Administration(CMA)and the ECMWF in forecasting the Meiyu characteristics in 2023 over the YHRB.Results show that the ECMWF GEPS reasonably forecasts the Meiyu rainfall,while the CMA GEPS presents a notable underestimation.The predictable lead time of the Meiyu onset date is eight days by the ECMWF GEPS and six days by the CMA GEPS,respectively.Regarding the regional rainstorm processes,the two GEPSs generally provide a predictable lead time of 48-168 h for reasonably forecasting the patterns of the heavy rainfall area.To further examine their strengths and weaknesses in Meiyu forecasting,this paper revisits their abilities in forecasting key influence systems.By verifying against their respective analyses,it is demonstrated that the ECMWF GEPS reasonably forecasts the spatial coverages of the northwestern Pacific subtropical high(NWPSH)and the South Asian high(SAH),whereas the CMA GEPS presents substantial underestimation.Both GEPSs show generally southward deviations for the eastern ridge line position(RLP)of the SAH,and exhibit a northward deviation for the western RLP of the NWPSH during early forecast lead times.The less Meiyu rainfall predicted by the CMA GEPS compared to the ECMWF GEPS can be attributed to its weaker low-level convergence belt and weaker upper-level divergence area.A deeper exploration into these forecast discrepancies in upper-level divergence and lower-level convergence suggests that they likely originate from their initial analysis fields.
文摘Implementing a CO2 flooding scheme successfully requires the capacity to get accurate information of reservoir dynamic performance and fluids injected. Despite some numerical simulation studies, the complicated drive mechanisms and actual reservoir performance have not been fully understood. There is a strong need to develop models from different perspectives to complement current simulators and provide valuable insights into the reservoir performance during CO2 flooding. The aim of this study is to develop a model by using an improved material balance equation （MBE） to analyze quickly the performance of CO2 flooding. After matching the historical field data the proposed model can be used to evaluate, monitor and predict the overall reservoir dynamic performance during CO2 flooding. In order to account accurately for the complex displacement process involving compositional effect and multiphase flow, the PVT properties and flowability of reservoir fluids are incorporated in the model. This study investigates the effects of a number of factors, such as reservoir pressure, the amount of CO2 injected, the CO2 partition ratios in reservoir fluids, the possibility of the existence of a free CO2 gas cap, the proportion of reservoir fluids contacted with CO2, the starting time of CO2 flooding, oil swelling, and oil flowability improvement by mixing with CO2. The model was used to analyze the CO2 flooding project in Weyburn oil field, Saskatchewan, Canada. This study shows that the proposed model is an effective complementary tool to analyze and monitor the overall reservoir performance during CO2 flooding.
基金the National Science and Technology Supporting Program during the 11th Five-year Plan Period of China(No.2006BAJ18B02)
文摘Traffic forecasting provides the estimation of future traffic state to help traffic control,travel guide,etc. This paper compared several widely used traffic forecasting methods,and analyzed each one's performance in detail to make conclusions,which could redound to researchers choosing an appropriate traffic forecasting method in their own works. Compared with conventional works,this paper creatively assessed the performance of traffic forecasting methods based on travel time index (TTI) data prediction,which made the accuracy of our comparison better.
文摘As after sales services become more and more popular,particularly preventive or corrective maintenance,the intervention and repair of the customer’s goods in a timely and efficient manner ensure customer satisfaction and contribute to the establishment of brand image in the market of the suppliers.The availability and quality of spare parts are key elements of this strategy while ensuring minimal management costs.The reuse of spare parts retrieved from customer systems is a growing maintenance strategy practice which impacts the traditional spare parts supply chain.This reuse is primarily driven by extending the economic life of goods,initially regarded as waste and therefore without added value,by transforming them into valuable spare parts that can be reused;secondly,for environmental or regulatory reasons,demanding responsibility for the treatment of products at the end of their life;and thirdly,to improve the availability of parts for maintenance,especially parts that the organization can no longer purchase or that are impacted by other issues.It also involves the analysis of their condition and their eventual return to working order as they are retrieved from the customer’s systems in a defective condition.In this paper,we will identify and classify the different customers and spare parts by estimating the critical level of rationing policy based on forecasts,identify the thresholds of inventory management policies,and finally,compare the different policies by service level and inventory level performance for the N.A.C.C.company.
基金supported by COMPETE:POCI-01-0247-FEDER-039719 and FCT-Fundação para a Ciência e Tecnologia within the Project Scope:UIDB/00127/2020.
文摘In this study,we address a demanding time series forecasting problem that deals simultaneously with the following:(1)intermittent time series,(2)multi-step ahead forecasting,(3)time series with multiple seasonal periods,and(4)performance measures for model selection across multiple time series.Current literature deals with these types of problems separately,and no study has dealt with all these characteristics simultaneously.To fill this knowledge gap,we begin by reviewing all the necessary existing literature relevant to this case study with the goal of proposing a framework capable of achieving adequate forecast accuracy for such a complex problem.Several adaptions and innovations have been conducted,which are marked as contributions to the literature.Specifically,we proposed a weighted average forecast combination of many cutting-edge models based on their out-of-sample performance.To gather strong evidence that our ensemble model works in practice,we undertook a large-scale study across 98 time series,rigorously assessed with unbiased performance measures,where a week seasonal naïve was set as a benchmark.The results demonstrate that the proposed ensemble model achieves eyecatching forecasting accuracy.
基金supported by the Science and Technology Plan of Shandong Transportation Department(No.2021B47)the Key Research and Development Program of Ningxia Science and Technology Department(No.2022BEG02008)the Fundamental Research Funds for the Central Universities(No.22120210027).
文摘Accurate pavement performance prediction plays a critical role in formulating maintenance and repair strategies for transportation departments,enabling the achievement of better pavement performance with limited financial resources.However,due to the intricate influence of numerous factors on pavement performance deterioration,improving the accuracy of pavement performance prediction poses a challenge for conventional models.Therefore,the aim of this study is to establish a machine learning-based pavement performance prediction model.First,this study considers five factors that affect pavement performance,including pavement initial performance indicators,traffic loads,weather,pavement structure,and maintenance measures,and identifies 15 specific indicators that affect pavement performance based on these five factors.Then,based on the the long-term pavement performance(LTPP)database,the study screens and summarizes these indicators,obtaining 2464 high-quality pavement performance data for pavement conditions index(PCI)prediction and 3238 high-quality pavement performance data for international roughness index(IRI)prediction.Finally,three distinct prediction models are established,namely,the fully connected neural network(FCNN)model,the long short-term memory(LSTM)model,and the combined LSTM-attention model.The study shows that the LSTM-attention model performs significantly better than the FCNN and LSTM models,with an R2 coefficient of determination of 0.81 for PCI and 0.79 for IRI.The innovation of this paper is that the authors have introduced the attention mechanism on the basic of the LSTM model,which makes the fitting accuracy of the prediction model further improved.
基金co-funded with ERDF fundsthe INTERREG MAC 2014-2020 programme,within the ENERMAC project(No.MAC/1.1a/117)。
文摘Due to the low dispatchability of wind power,the massive integration of this energy source in power systems requires short-term and very short-term wind power output forecasting models to be as efficient and stable as possible.A study is conducted in the present paper of potential improvements to the performance of artificial neural network(ANN)models in terms of efficiency and stability.Generally,current ANN models have been developed by considering exclusively the meteorological information of the wind farm reference station,in addition to selecting a fixed number of time periods prior to the forecasting.In this respect,new ANN models are proposed in this paper,which are developed by:varying the number of prior 1-h periods(periods prior to the forecasting hour)chosen for the input layer parameters;and/or incorporating in the input layer data from a second weather station in addition to the wind farm reference station.It has been found that the model performance is always improved when data from a second weather station are incorporated.The mean absolute relative error(MARE)of the new models is reduced by up to 7.5%.Furthermore,the longer the forecasting horizon,the greater the degree of improvement.
基金This work was supported partly by National Natural Science Foundation of China under Grant No.79990583 and 70221001
文摘This paper presents a new index system for the performance evaluation and network planning of multimedia communication systems using measurement on actual systems to support several different traffic types. In this index system, we develop an expert system to evaluate the performance of such multimedia communication networks including channel utilization and call blocking probability and packet delay, and apply the network planning methods to optimize the networks and forecast the demand of the growing multimedia communications systems. Two important planning problems for the multimedia communication systems are presented: optimization problem for construction of the world system and forecast problem for increasing traffic demands. We first discuss analysis methods, performance measures for the multimedia communication systems. Then, we describe network planning methods for the multimedia communication systems and give some efficiency network planning methods. Finally, we present some results studied in traffic forecast for the campus network and show the effectiveness of these methods.
基金National Key R&D Program of China(2019YFC1510205)Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province(SCSF202202)+1 种基金Shenzhen Science and Technology Project(KCXFZ2020122173610028)Jiangsu Collaborative Innovation Center for Climate Change。
文摘In recent work,three physical factors of the Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation(DSAEF_LTP model)have been introduced,namely,tropical cyclone(TC)track,TC landfall season,and TC intensity.In the present study,we set out to test the forecasting performance of the improved model with new similarity regions and ensemble forecast schemes added.Four experiments associated with the prediction of accumulated precipitation were conducted based on 47 landfalling TCs that occurred over South China during 2004-2018.The first experiment was designed as the DSAEF_LTP model with TC track,TC landfall season,and intensity(DSAEF_LTP-1).The other three experiments were based on the first experiment,but with new ensemble forecast schemes added(DSAEF_LTP-2),new similarity regions added(DSAEF_LTP-3),and both added(DSAEF_LTP-4),respectively.Results showed that,after new similarity regions added into the model(DSAEF_LTP-3),the forecasting performance of the DSAEF_LTP model for heavy rainfall(accumulated precipitation≥250 mm and≥100 mm)improved,and the sum of the threat score(TS250+TS100)increased by 4.44%.Although the forecasting performance of DSAEF_LTP-2 was the same as that of DSAEF_LTP-1,the forecasting performance was significantly improved and better than that of DSAEF_LTP-3 when the new ensemble schemes and similarity regions were added simultaneously(DSAEF_LTP-4),with the TS increasing by 25.36%.Moreover,the forecasting performance of the four experiments was compared with four operational numerical weather prediction models,and the comparison indicated that the DSAEF_LTP model showed advantages in predicting heavy rainfall.Finally,some issues associated with the experimental results and future improvements of the DSAEF_LTP model were discussed.
基金supported by the National Natural Science Foundation of China (Grant Nos. 42025404, 42188101, and 42241143)the National Key R&D Program of China (Grant Nos. 2022YFF0503700 and 2022YFF0503900)+1 种基金the B-type Strategic Priority Program of the Chinese Academy of Sciences (Grant No. XDB41000000)the Fundamental Research Funds for the Central Universities (Grant No. 2042022kf1012)
文摘Because radiation belt electrons can pose a potential threat to the safety of satellites orbiting in space,it is of great importance to develop a reliable model that can predict the highly dynamic variations in outer radiation belt electron fluxes.In the present study,we develop a forecast model of radiation belt electron fluxes based on the data assimilation method,in terms of Van Allen Probe measurements combined with three-dimensional radiation belt numerical simulations.Our forecast model can cover the entire outer radiation belt with a high temporal resolution(1 hour)and a spatial resolution of 0.25 L over a wide range of both electron energy(0.1-5.0 MeV)and pitch angle(5°-90°).On the basis of this model,we forecast hourly electron fluxes for the next 1,2,and 3 days during an intense geomagnetic storm and evaluate the corresponding prediction performance.Our model can reasonably predict the stormtime evolution of radiation belt electrons with high prediction efficiency(up to~0.8-1).The best prediction performance is found for~0.3-3 MeV electrons at L=~3.25-4.5,which extends to higher L and lower energies with increasing pitch angle.Our results demonstrate that the forecast model developed can be a powerful tool to predict the spatiotemporal changes in outer radiation belt electron fluxes,and the model has both scientific significance and practical implications.
