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
Accurate estimation of land surface solar irradiation is critical for effective solar energy utilization and planning of solar photovoltaic planning.Although traditional machine learning methods have been demonstrated...Accurate estimation of land surface solar irradiation is critical for effective solar energy utilization and planning of solar photovoltaic planning.Although traditional machine learning methods have been demonstrated to estimate solar irradiation effectively,they face challenges in modeling over large regions,as well as lacking of ability to model spatial diversity and temporal dynamics of solar irradiation,and providing limited interpretability.To address these limitations,this study proposed a geospatial artificial intelligence framework augmented by Temporal Fusion Transformer for hourly estimation of land surface solar irradiation.As a case study in Australia,the results demonstrate superior performance with the coefficient of the determination,the mean absolute error,and Root Mean Square Error as high as 0.90,0.25(kWh/m^(2)),and 0.63(kWh/m^(2)),showing improvements of 21.62–66.67%,78.37–85.98%,and 62.81–73.25%,respectively,compared to the benchmarks of other methods,including Support Vector Regression,Random Forest,Gradient Boosting Machine,AdaBoost,Long Short-Term Memory,Temporal Convolutional Network,ConvLSTM,Transformer,and Graph Neural Network.Furthermore,interpretability results of the model indicate that among the temporal variables,observed solar irradiation and clear sky solar irradiation significantly contribute to the model’s performance.The results show this framework enhanced accuracy and interpretability for solar irradiation estimation over large areas,providing valuable insights for future studies and supporting decision-making for developing the renewable energy industry.展开更多
Building integrated energy systems(BIESs)are pivotal for enhancing energy efficiency by accounting for a significant proportion of global energy consumption.Two key barriers that reduce the BIES operational efficiency...Building integrated energy systems(BIESs)are pivotal for enhancing energy efficiency by accounting for a significant proportion of global energy consumption.Two key barriers that reduce the BIES operational efficiency mainly lie in the renewable generation uncertainty and operational non-convexity of combined heat and power(CHP)units.To this end,this paper proposes a soft actor-critic(SAC)algorithm to solve the scheduling problem of BIES,which overcomes the model non-convexity and shows advantages in robustness and generalization.This paper also adopts a temporal fusion transformer(TFT)to enhance the optimal solution for the SAC algorithm by forecasting the renewable generation and energy demand.The TFT can effectively capture the complex temporal patterns and dependencies that span multiple steps.Furthermore,its forecasting results are interpretable due to the employment of a self-attention layer so as to assist in more trustworthy decision-making in the SAC algorithm.The proposed hybrid data-driven approach integrating TFT and SAC algorithm,i.e.,TFT-SAC approach,is trained and tested on a real-world dataset to validate its superior performance in reducing the energy cost and computational time compared with the benchmark approaches.The generalization performance for the scheduling policy,as well as the sensitivity analysis,are examined in the case studies.展开更多
Some results and developments on the extension of the inverse scattering transform to solve non-linear evolution equations in one time and two space dimensions are described.
Increased penetration of renewables for power generation has negatively impacted the dynamics of conventional fossil fuel-based power plants.The power plants operating on the base load are forced to cycle,to adjust to...Increased penetration of renewables for power generation has negatively impacted the dynamics of conventional fossil fuel-based power plants.The power plants operating on the base load are forced to cycle,to adjust to the fluctuating power demands.This results in an inefficient operation of the coal power plants,which leads up to higher operating losses.To overcome such operational challenge associated with cycling and to develop an optimal process control,this work analyzes a set of models for predicting power generation.Moreover,the power generation is intrinsically affected by the state of the power plant components,and therefore our model development also incorporates additional power plant process variables while forecasting the power generation.We present and compare multiple state-of-the-art forecasting data-driven methods for power generation to determine the most adequate and accurate model.We also develop an interpretable attention-based transformer model to explain the importance of process variables during training and forecasting.The trained deep neural network(DNN)LSTM model has good accuracy in predicting gross power generation under various prediction horizons with/without cycling events and outperforms the other models for long-term forecasting.The DNN memory-based models show significant superiority over other state-of-the-art machine learning models for short,medium and long range predictions.The transformer-based model with attention enhances the selection of historical data for multi-horizon forecasting,and also allows to interpret the significance of internal power plant components on the power generation.This newly gained insights can be used by operation engineers to anticipate and monitor the health of power plant equipment during high cycling periods.展开更多
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.
基金substantially funded by the General Research Fund(Grant No.15603923 and 15603920)the Collaborative Research Fund(Grant No.C5062-21GF)+1 种基金Young Collaborate Research Fund(Grant No.C6003-22Y)from the Research Grants Council,Hong Kong,Chinafunding support(Grant No.BBG2 and CD81)from the Research Institute for Sustainable Urban Develop-ment,Research Institute of Land and Space,The Hong Kong Polytechnic University,Kowloon,Hong Kong,China.
文摘Accurate estimation of land surface solar irradiation is critical for effective solar energy utilization and planning of solar photovoltaic planning.Although traditional machine learning methods have been demonstrated to estimate solar irradiation effectively,they face challenges in modeling over large regions,as well as lacking of ability to model spatial diversity and temporal dynamics of solar irradiation,and providing limited interpretability.To address these limitations,this study proposed a geospatial artificial intelligence framework augmented by Temporal Fusion Transformer for hourly estimation of land surface solar irradiation.As a case study in Australia,the results demonstrate superior performance with the coefficient of the determination,the mean absolute error,and Root Mean Square Error as high as 0.90,0.25(kWh/m^(2)),and 0.63(kWh/m^(2)),showing improvements of 21.62–66.67%,78.37–85.98%,and 62.81–73.25%,respectively,compared to the benchmarks of other methods,including Support Vector Regression,Random Forest,Gradient Boosting Machine,AdaBoost,Long Short-Term Memory,Temporal Convolutional Network,ConvLSTM,Transformer,and Graph Neural Network.Furthermore,interpretability results of the model indicate that among the temporal variables,observed solar irradiation and clear sky solar irradiation significantly contribute to the model’s performance.The results show this framework enhanced accuracy and interpretability for solar irradiation estimation over large areas,providing valuable insights for future studies and supporting decision-making for developing the renewable energy industry.
文摘Building integrated energy systems(BIESs)are pivotal for enhancing energy efficiency by accounting for a significant proportion of global energy consumption.Two key barriers that reduce the BIES operational efficiency mainly lie in the renewable generation uncertainty and operational non-convexity of combined heat and power(CHP)units.To this end,this paper proposes a soft actor-critic(SAC)algorithm to solve the scheduling problem of BIES,which overcomes the model non-convexity and shows advantages in robustness and generalization.This paper also adopts a temporal fusion transformer(TFT)to enhance the optimal solution for the SAC algorithm by forecasting the renewable generation and energy demand.The TFT can effectively capture the complex temporal patterns and dependencies that span multiple steps.Furthermore,its forecasting results are interpretable due to the employment of a self-attention layer so as to assist in more trustworthy decision-making in the SAC algorithm.The proposed hybrid data-driven approach integrating TFT and SAC algorithm,i.e.,TFT-SAC approach,is trained and tested on a real-world dataset to validate its superior performance in reducing the energy cost and computational time compared with the benchmark approaches.The generalization performance for the scheduling policy,as well as the sensitivity analysis,are examined in the case studies.
文摘Some results and developments on the extension of the inverse scattering transform to solve non-linear evolution equations in one time and two space dimensions are described.
文摘Increased penetration of renewables for power generation has negatively impacted the dynamics of conventional fossil fuel-based power plants.The power plants operating on the base load are forced to cycle,to adjust to the fluctuating power demands.This results in an inefficient operation of the coal power plants,which leads up to higher operating losses.To overcome such operational challenge associated with cycling and to develop an optimal process control,this work analyzes a set of models for predicting power generation.Moreover,the power generation is intrinsically affected by the state of the power plant components,and therefore our model development also incorporates additional power plant process variables while forecasting the power generation.We present and compare multiple state-of-the-art forecasting data-driven methods for power generation to determine the most adequate and accurate model.We also develop an interpretable attention-based transformer model to explain the importance of process variables during training and forecasting.The trained deep neural network(DNN)LSTM model has good accuracy in predicting gross power generation under various prediction horizons with/without cycling events and outperforms the other models for long-term forecasting.The DNN memory-based models show significant superiority over other state-of-the-art machine learning models for short,medium and long range predictions.The transformer-based model with attention enhances the selection of historical data for multi-horizon forecasting,and also allows to interpret the significance of internal power plant components on the power generation.This newly gained insights can be used by operation engineers to anticipate and monitor the health of power plant equipment during high cycling periods.
