Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk...Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk management.This study aims to use deep learning to develop real-time models for predicting the penetration rate(PR).The models are built using data from the Changsha metro project,and their performances are evaluated using unseen data from the Zhengzhou Metro project.In one-step forecast,the predicted penetration rate follows the trend of the measured penetration rate in both training and testing.The autoregressive integrated moving average(ARIMA)model is compared with the recurrent neural network(RNN)model.The results show that univariate models,which only consider historical penetration rate itself,perform better than multivariate models that take into account multiple geological and operational parameters(GEO and OP).Next,an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed,and it performs better than other models.A sensitivity analysis shows that the penetration rate is the most important parameter,while other parameters have a smaller impact on time series forecasting.It is also found that smoothed data are easier to predict with high accuracy.Nevertheless,over-simplified data can lose real characteristics in time series.In conclusion,the RNN variant can accurately predict the next-step penetration rate,and data smoothing is crucial in time series forecasting.This study provides practical guidance for TBM performance forecasting in practical engineering.展开更多
For the assessment and management of regional to local air quality, an integrated environmental management information system was built within the multi national Eureka project 3266 Webair, http：//www.ess.co.at/WEBAI...For the assessment and management of regional to local air quality, an integrated environmental management information system was built within the multi national Eureka project 3266 Webair, http：//www.ess.co.at/WEBAIR. The system combines data bases and GIS and a range of coupled models and analytical tools that address a range of typical management problems and cover several levels of nesting from regional to city level and street canyons. The main functions are to support regulatory tasks, compliance monitoring, operational forecasting and reporting, impact assessment EIA （environmental impact assessment）, SEA （strategic environmental assessment） and public information within one consistent framework. A major objective is the improvement of air quality through emission control. The integrated model system together with its shared data bases provides a reliable, consistent basis for the non-linear techno-economic and multi-criteria optimization of emission control strategies （including greenhouse gases and energy efficiency）. A real-time expert system drives, supports and monitors the autonomous and interactive operations, and provides embedded QA/QC （quality assurance/quality control） functions for reliable operations and ease of use.展开更多
Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning ...Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.展开更多
Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep...Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.展开更多
Accurate and reliable flood forecast is crucial for efficient real-time river management, including flood control, flood warning, reservoir operation and river regulation. In order to improve the estimate of the initi...Accurate and reliable flood forecast is crucial for efficient real-time river management, including flood control, flood warning, reservoir operation and river regulation. In order to improve the estimate of the initial state of the forecasting system and to reduce the errors in the forecast period a data assimilation procedure was often need. The Kalman filter was proven to be an efficient method to adjust real-time flood series and improve the initial conditions before the forecast. A new model integrating the hydraulic model with the Kalman filter for real-time correction of flood forecast was developed and applied in the Three Gorges interzone of the Yangtze River. The method was calibrated and verified against the observed flood stage and discharge during Three Gorges Dam construction periods （2004）. The results demonstrate that the new model incorporates an accurate and fast updating technique can improve the reliability of the flood forecast.展开更多
This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts,which are integral to an optimal energy storage control system.By expanding on...This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts,which are integral to an optimal energy storage control system.By expanding on an existing algorithm,this study resolves issues discovered during implementation and addresses previously overlooked concerns,resulting in significant enhancements in both performance and reliability.The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model,which is utilized for illustrative simulations to highlight its potential efficacy on a real site.Furthermore,a comprehensive comparison with the original formulation was conducted to cover all possible scenarios.This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system.Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management,significant financial losses and penalties,and potential contract violations.The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements.These advancements yield a reliable and efficient real-time correction algorithm for optimal site management,designed as an independent white box that can be integrated with any day-ahead optimization control system.展开更多
A combination of the rainfall-runoff module of the Xin’anjiang model, the Muskingum routing method, the water stage simulating hydrologic method, the diffusion wave nonlinear water stage method, and the real-time err...A combination of the rainfall-runoff module of the Xin’anjiang model, the Muskingum routing method, the water stage simulating hydrologic method, the diffusion wave nonlinear water stage method, and the real-time error correction method is applied to the real-time flood forecasting and regulation of the Huai River with flood diversion and retarding areas. The Xin’anjiang model is used to forecast the flood discharge hydrograph of the upstream and tributary. The flood routing of the main channel and flood diversion areas is based on the Muskingum method. The water stage of the downstream boundary condition is calculated with the water stage simulating hydrologic method and the water stages of each cross section are calculated from downstream to upstream with the diffusion wave nonlinear water stage method. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The faded-memory forgetting factor least square of error series is used as the real-time error correction method for forecasting discharge and water stage. As an example, the combined models were applied to flood forecasting and regulation of the upper reaches of the Huai River above Lutaizi during the 2007 flood season. The forecast achieves a high accuracy and the results show that the combined models provide a scientific way of flood forecasting and regulation for a complex watershed with flood diversion and retarding areas.展开更多
Traffic congestions and road accidents continue to increase in industry countries. There are three basic strategies to relieve congestion. The first strategy is to increase the transportation infrastructure. However, ...Traffic congestions and road accidents continue to increase in industry countries. There are three basic strategies to relieve congestion. The first strategy is to increase the transportation infrastructure. However, this strategy is very expensive and can only be accomplished in the long-term. The second strategy is to limit the traffic demand or make traveling more expensive that will be strongly opposed by travelers. The third strategy is to focus on efficient and intelligent utilization of the existing transportation infrastructures. This strategy is gaining more and more attention because it’s well. Currently, the Intelligent Transportation System (ITS) is the most promising approach to implementing the third strategy. Various forecast schemes have been proposed to manage the traffic data. Many studies showed that the moving average schemes offered meaningful results compared to different forecast schemes. This paper considered the moving average schemes, namely, simple moving average, weighted moving average, and exponential moving average. Furthermore, the performance analysis of the shortterm forecast schemes will be discussed. Moreover, the real-time forecast model will consider the abnormal condition detection.展开更多
Flood forecasting is critical for mitigating flood damage and ensuring a safe operation of hydroelectric power plants and reservoirs.This paper presents a new hybrid hydrological model based on the combination of the ...Flood forecasting is critical for mitigating flood damage and ensuring a safe operation of hydroelectric power plants and reservoirs.This paper presents a new hybrid hydrological model based on the combination of the Hydrologic Engineering Center-Hydrologic Modeling System(HEC-HMS)hydrological model and an Encoder-Decoder-Long Short-Term Memory network to enhance the accuracy of real-time flood forecasting.The proposed hybrid model has been applied to the Krong H'nang hydropower reservoir.The observed data from 33 floods monitored between 2016 and 2021 are used to calibrate,validate,and test the hybrid model.Results show that the HEC-HMS-artificial neural network hybrid model significantly improves the forecast quality,especially for results at a longer forecasting time.In detail,the Kling-Gupta efficiency(KGE)index,for example,increased from ΔKGE=16%at time t+1h to ΔKGE=69%at time t+6 h.Similar results were obtained for other indicators including peak error and volume error.The computer program developed for this study is being used in practice at the Krong H'nang hydropower to aid in reservoir planning,flood control,and water resource efficiency.展开更多
<strong>Introduction:</strong> More than a million people are reported to have been infected with COVID in India, since the beginning of the pandemic. However, the epidemic is not the same across the count...<strong>Introduction:</strong> More than a million people are reported to have been infected with COVID in India, since the beginning of the pandemic. However, the epidemic is not the same across the country. Though there are state-level variations rapidly changing disease dynamics and the response has created uncertainty towards appropriate use of models to project for the future. <strong>Method:</strong> This paper aims at using a validated semi-mechanistic stochastic model to generate short term forecasts. This analysis used data available at the respective state government bulletins for four states. The analysis used a simplified transmission model using Markov Chain Monte Carlo simulation with Metropolis-Hastings updating. <strong>Results:</strong> Two weeks were used to compare the results with the actual data. The forecasted results are well within the 25<sup>th</sup> and 75<sup>th</sup> percentile of the actual cases reported by the respective states. The results indicate a reliable method for a real-time short term forecasting of COVID-19 cases. The 1st week projected interquartile range and actual;reported cases for the state of Kerala, Tamil Nadu, Andhra Pradesh and Odisha were (1064 - 2532) 2234, (17,503 - 50,125) 27,214, (5225 - 11,003) 9563, (2559 - 4461) 3925, respectively. Similarly, the 2<sup>nd</sup> week projected interquartile range and actual;reported cases were (1055 - 7803) 4221, (18,298 - 73,952) 31,488, (4705 - 23,224) 13,357, (2701 - 9037) 4175 respectively. <strong>Conclusion:</strong> This real-time forecast can be used as an early warning tool for projecting the changes in the epidemic in the near future triggering proactive management steps.展开更多
Real-time electricity price(RTEP)influence factor extraction is essential to forecasting accurate power system electricity prices.At present,new electricity price forecasting models have been studied to improve predic...Real-time electricity price(RTEP)influence factor extraction is essential to forecasting accurate power system electricity prices.At present,new electricity price forecasting models have been studied to improve predictive accuracy,ignoring the extraction and analysis of RTEP influence factors.In this study,a correlation analysis method is proposed based on stochastic matrix theory.Firstly,an augmented matrix is formulated,including RTEP influence factor data and RTEP state data.Secondly,data correlation analysis results are obtained given the statistical characteristics of source data based on stochastic matrix theory.Mean spectral radius(MSR)is used as the measure of correlativity.Finally,the proposed method is evaluated in New England electricity markets and compared with the BP neural network forecasting method.Experimental results show that the extracted index system comprehensively generalizes RTEP influence factors,which play a significant role in improving RTEP forecasting accuracy.展开更多
Today,urban traffic,growing populations,and dense transportation networks are contributing to an increase in traffic incidents.These incidents include traffic accidents,vehicle breakdowns,fires,and traffic disputes,re...Today,urban traffic,growing populations,and dense transportation networks are contributing to an increase in traffic incidents.These incidents include traffic accidents,vehicle breakdowns,fires,and traffic disputes,resulting in long waiting times,high carbon emissions,and other undesirable situations.It is vital to estimate incident response times quickly and accurately after traffic incidents occur for the success of incident-related planning and response activities.This study presents a model for forecasting the traffic incident duration of traffic events with high precision.The proposed model goes through a 4-stage process using various features to predict the duration of four different traffic events and presents a feature reduction approach to enable real-time data collection and prediction.In the first stage,the dataset consisting of 24,431 data points and 75 variables is prepared by data collection,merging,missing data processing and data cleaning.In the second stage,models such as Decision Trees(DT),K-Nearest Neighbour(KNN),Random Forest(RF)and Support Vector Machines(SVM)are used and hyperparameter optimisation is performed with GridSearchCV.In the third stage,feature selection and reduction are performed and real-time data are used.In the last stage,model performance with 14 variables is evaluated with metrics such as accuracy,precision,recall,F1-score,MCC,confusion matrix and SHAP.The RF model outperforms other models with an accuracy of 98.5%.The study’s prediction results demonstrate that the proposed dynamic prediction model can achieve a high level of success.展开更多
Using real-time correction technology for typhoons, this paper discusses real-time correction for forecasting the track of four typhoons during 2009 and 2010 in Japan, Beijing, Guangzhou, and Shanghai. It was determin...Using real-time correction technology for typhoons, this paper discusses real-time correction for forecasting the track of four typhoons during 2009 and 2010 in Japan, Beijing, Guangzhou, and Shanghai. It was determined that the short-time forecast effect was better than the original objective mode. By selecting four types of integration schemes after multiple mode path integration for those four objective modes, the forecast effect of the multi-mode path integration is better, on average, than any single model. Moreover, multi-mode ensemble forecasting has obvious advantages during the initial 36 h.展开更多
This paper reports the use of a specialized, mesoscale, numerical weather prediction （NWP） system and a satellite imaging and prediction system that were set up to support the CLAMS （Chesapeake Lighthouse and Aircr...This paper reports the use of a specialized, mesoscale, numerical weather prediction （NWP） system and a satellite imaging and prediction system that were set up to support the CLAMS （Chesapeake Lighthouse and Aircraft Measurements for Satellites） field campaign during the summer of 2001. The primary objective of CLAMS was to validate satellite-based retrievals of aerosol properties and vertical profiles of the radiative flux, temperature and water vapor. Six research aircraft were deployed to make detailed coincident measurements of the atmosphere and ocean surface with the research satellites that orbited overhead. The mesoscale weather modeling system runs in real-time to provide high spatial and temporal resolution for forecasts that are delivered via the World Wide Web along with a variety of satellite imagery and satellite location predictions. This system is a multi-purpose modeling system capable of both data analysis/assimilation and multi-scale NWP ranging from cloud-scale to larger than regional scale. This is a three-dimensional, non-hydrostatic compressible model in a terrain-following coordinate. The model employs advanced numerical techniques and contains detailed interactive physical processes. The utility of the forecasting system is illustrated throughout the discussion on the impact of the surface-wind forecast on BRDF （Bidirectional Reflectance Distribution Function） and the description of the cloud/moisture forecast versus the aircraft measurement.展开更多
Ocean Renewable Energy(ORE)systems—comprising wind,wave,tidal,and ocean thermal energy—are increasingly seen as viable alternatives to fossil fuels.However,their integration into the power grid is hindered by enviro...Ocean Renewable Energy(ORE)systems—comprising wind,wave,tidal,and ocean thermal energy—are increasingly seen as viable alternatives to fossil fuels.However,their integration into the power grid is hindered by environmental sensitivity,dynamic ocean conditions,and high maintenance demands.Artificial Intelligence(AI)offers promising solutions to these challenges by enabling intelligent,adaptive,and resilient energy systems.This review explores AI applications in ORE,focusing on three critical domains:optimization,forecasting,and control.Optimization techniques,including Genetic Algorithms(GA)and Swarm Intelligence(SI),are employed to enhance device efficiency,improve energy capture,optimize farm layouts,reduce environmental impacts,and lower installation costs.Forecasting uses Machine Learning(ML)and Deep Learning(DL)models to predict wave height,tidal flow,and energy output,aiding in grid integration and energy scheduling.In control systems,AI approaches like Reinforcement Learning(RL)and Fuzzy Logic ensure real-time responsiveness and predictive maintenance,improving system reliability in dynamic marine environments.Emerging technologies such as Edge AI enable decentralized computation for real-time decision-making,while Digital Twin frameworks simulate and predict system performance before deployment.Explainable AI(XAI)is also discussed to ensure transparent and trustworthy decision-making.Ethical and regulatory concerns are acknowledged to ensure responsible AI integration in ocean settings.Overall this review offers a comprehensive synthesis of how AI enhances the performance,efficiency,and scalability of ORE systems.It serves as a valuable resource for researchers,policymakers,and industry professionals seeking to advance clean,smart,and sustainable ocean energy solutions.展开更多
Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficie...Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation.This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies:Hybrid-Si,Mono-Si,and Poly-Si,across three forecasting horizons:1-step,12-step,and 24-step.Among the tested models,the Convolutional Neural Network—Long Short-Term Memory(CNN-LSTM)architecture exhibited superior performance,particularly for the 24-step horizon,achieving R^(2)=0.9793 and MAE 0.0162 for the Poly-Si array,followed by Mono-Si(R^(2)=0.9768)and Hybrid-Si arrays(R^(2)=0.9769).These findings demonstrate that the CNN-LSTM model can provide accurate and reliable PV power predictions for all studied technologies.By identifying the most suitable predictive model for each panel technology,this study contributes to optimizing PV power forecasting and improving energy management strategies.展开更多
Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predict...Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predictability,deep learning has yet to be sufficiently explored for complex deformation patterns associated with landslides and is inherently opaque.Herein,we developed a holistic landslide deformation forecasting method that considers spatiotemporal correlations of landslide deformation by integrating domain knowledge into interpretable deep learning.By spatially capturing the interconnections between multiple deformations from different observation points,our method contributes to the understanding and forecasting of landslide systematic behavior.By integrating specific domain knowledge relevant to each observation point and merging internal properties with external variables,the local heterogeneity is considered in our method,identifying deformation temporal patterns in different landslide zones.Case studies involving reservoir-induced landslides and creeping landslides demonstrated that our approach(1)enhances the accuracy of landslide deformation forecasting,(2)identifies significant contributing factors and their influence on spatiotemporal deformation characteristics,and(3)demonstrates how identifying these factors and patterns facilitates landslide forecasting.Our research offers a promising and pragmatic pathway toward a deeper understanding and forecasting of complex landslide behaviors.展开更多
The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for he...The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for healthcare systems,particularly for identifying actions critical to patient well-being.However,challenges such as high computational demands,low accuracy,and limited adaptability persist in Human Motion Recognition(HMR).While some studies have integrated HMR with IoT for real-time healthcare applications,limited research has focused on recognizing MRHA as essential for effective patient monitoring.This study proposes a novel HMR method tailored for MRHA detection,leveraging multi-stage deep learning techniques integrated with IoT.The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions(MBConv)blocks,followed by Convolutional Long Short Term Memory(ConvLSTM)to capture spatio-temporal patterns.A classification module with global average pooling,a fully connected layer,and a dropout layer generates the final predictions.The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets,focusing on MRHA such as sneezing,falling,walking,sitting,etc.It achieves 94.85%accuracy for cross-subject evaluations and 96.45%for cross-view evaluations on NTU RGB+D 120,along with 89.22%accuracy on HMDB51.Additionally,the system integrates IoT capabilities using a Raspberry Pi and GSM module,delivering real-time alerts via Twilios SMS service to caregivers and patients.This scalable and efficient solution bridges the gap between HMR and IoT,advancing patient monitoring,improving healthcare outcomes,and reducing costs.展开更多
Accurate energy demand forecasting is crucial in today’s rapidly electrifying world with decentralized systems and integrated renewables.Traditional models struggle with the dynamic complexities,but AI(artificial int...Accurate energy demand forecasting is crucial in today’s rapidly electrifying world with decentralized systems and integrated renewables.Traditional models struggle with the dynamic complexities,but AI(artificial intelligence),particularly ML(machine learning)and DL(deep learning),offers transformative solutions.This article explores how AI enhances forecasting accuracy,enables real-time adaptability,and supports strategic energy management.It examines the synergy between AI,IoT(Internet of Things)devices,and smart grids in generating predictive and prescriptive insights.Through case studies,we analyze the benefits and challenges of deploying AI in this domain,including data quality,model explainability,and infrastructure needs.Ultimately,AI emerges as a key enabler for the resilient,data-driven energy systems required to meet modern society’s evolving demands and achieve a sustainable future.展开更多
文摘Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk management.This study aims to use deep learning to develop real-time models for predicting the penetration rate(PR).The models are built using data from the Changsha metro project,and their performances are evaluated using unseen data from the Zhengzhou Metro project.In one-step forecast,the predicted penetration rate follows the trend of the measured penetration rate in both training and testing.The autoregressive integrated moving average(ARIMA)model is compared with the recurrent neural network(RNN)model.The results show that univariate models,which only consider historical penetration rate itself,perform better than multivariate models that take into account multiple geological and operational parameters(GEO and OP).Next,an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed,and it performs better than other models.A sensitivity analysis shows that the penetration rate is the most important parameter,while other parameters have a smaller impact on time series forecasting.It is also found that smoothed data are easier to predict with high accuracy.Nevertheless,over-simplified data can lose real characteristics in time series.In conclusion,the RNN variant can accurately predict the next-step penetration rate,and data smoothing is crucial in time series forecasting.This study provides practical guidance for TBM performance forecasting in practical engineering.
文摘For the assessment and management of regional to local air quality, an integrated environmental management information system was built within the multi national Eureka project 3266 Webair, http：//www.ess.co.at/WEBAIR. The system combines data bases and GIS and a range of coupled models and analytical tools that address a range of typical management problems and cover several levels of nesting from regional to city level and street canyons. The main functions are to support regulatory tasks, compliance monitoring, operational forecasting and reporting, impact assessment EIA （environmental impact assessment）, SEA （strategic environmental assessment） and public information within one consistent framework. A major objective is the improvement of air quality through emission control. The integrated model system together with its shared data bases provides a reliable, consistent basis for the non-linear techno-economic and multi-criteria optimization of emission control strategies （including greenhouse gases and energy efficiency）. A real-time expert system drives, supports and monitors the autonomous and interactive operations, and provides embedded QA/QC （quality assurance/quality control） functions for reliable operations and ease of use.
文摘Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.
基金supported by the National Natural Science Foundation of China[grant number 62376217]the Young Elite Scientists Sponsorship Program by CAST[grant number 2023QNRC001]the Joint Research Project for Meteorological Capacity Improvement[grant number 24NLTSZ003]。
文摘Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.
基金the National Science and Technology Planning (Grant No. 2006BAC05B02)
文摘Accurate and reliable flood forecast is crucial for efficient real-time river management, including flood control, flood warning, reservoir operation and river regulation. In order to improve the estimate of the initial state of the forecasting system and to reduce the errors in the forecast period a data assimilation procedure was often need. The Kalman filter was proven to be an efficient method to adjust real-time flood series and improve the initial conditions before the forecast. A new model integrating the hydraulic model with the Kalman filter for real-time correction of flood forecast was developed and applied in the Three Gorges interzone of the Yangtze River. The method was calibrated and verified against the observed flood stage and discharge during Three Gorges Dam construction periods （2004）. The results demonstrate that the new model incorporates an accurate and fast updating technique can improve the reliability of the flood forecast.
基金supported by the Israeli Ministry of Infrastructure,Energy and Water Resources.
文摘This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts,which are integral to an optimal energy storage control system.By expanding on an existing algorithm,this study resolves issues discovered during implementation and addresses previously overlooked concerns,resulting in significant enhancements in both performance and reliability.The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model,which is utilized for illustrative simulations to highlight its potential efficacy on a real site.Furthermore,a comprehensive comparison with the original formulation was conducted to cover all possible scenarios.This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system.Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management,significant financial losses and penalties,and potential contract violations.The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements.These advancements yield a reliable and efficient real-time correction algorithm for optimal site management,designed as an independent white box that can be integrated with any day-ahead optimization control system.
基金supported by the National Natural Science Foundation of China (Grant No 50479017)the Program for Changjiang Scholars and Innovative Research Teams in Universities (Grant No IRT071)
文摘A combination of the rainfall-runoff module of the Xin’anjiang model, the Muskingum routing method, the water stage simulating hydrologic method, the diffusion wave nonlinear water stage method, and the real-time error correction method is applied to the real-time flood forecasting and regulation of the Huai River with flood diversion and retarding areas. The Xin’anjiang model is used to forecast the flood discharge hydrograph of the upstream and tributary. The flood routing of the main channel and flood diversion areas is based on the Muskingum method. The water stage of the downstream boundary condition is calculated with the water stage simulating hydrologic method and the water stages of each cross section are calculated from downstream to upstream with the diffusion wave nonlinear water stage method. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The faded-memory forgetting factor least square of error series is used as the real-time error correction method for forecasting discharge and water stage. As an example, the combined models were applied to flood forecasting and regulation of the upper reaches of the Huai River above Lutaizi during the 2007 flood season. The forecast achieves a high accuracy and the results show that the combined models provide a scientific way of flood forecasting and regulation for a complex watershed with flood diversion and retarding areas.
文摘Traffic congestions and road accidents continue to increase in industry countries. There are three basic strategies to relieve congestion. The first strategy is to increase the transportation infrastructure. However, this strategy is very expensive and can only be accomplished in the long-term. The second strategy is to limit the traffic demand or make traveling more expensive that will be strongly opposed by travelers. The third strategy is to focus on efficient and intelligent utilization of the existing transportation infrastructures. This strategy is gaining more and more attention because it’s well. Currently, the Intelligent Transportation System (ITS) is the most promising approach to implementing the third strategy. Various forecast schemes have been proposed to manage the traffic data. Many studies showed that the moving average schemes offered meaningful results compared to different forecast schemes. This paper considered the moving average schemes, namely, simple moving average, weighted moving average, and exponential moving average. Furthermore, the performance analysis of the shortterm forecast schemes will be discussed. Moreover, the real-time forecast model will consider the abnormal condition detection.
基金Vingroup JSCMaster,PhD Scholarship Program of Vingroup Innovation Foundation,Grant/Award Number:VINIF.2021.ThS.97。
文摘Flood forecasting is critical for mitigating flood damage and ensuring a safe operation of hydroelectric power plants and reservoirs.This paper presents a new hybrid hydrological model based on the combination of the Hydrologic Engineering Center-Hydrologic Modeling System(HEC-HMS)hydrological model and an Encoder-Decoder-Long Short-Term Memory network to enhance the accuracy of real-time flood forecasting.The proposed hybrid model has been applied to the Krong H'nang hydropower reservoir.The observed data from 33 floods monitored between 2016 and 2021 are used to calibrate,validate,and test the hybrid model.Results show that the HEC-HMS-artificial neural network hybrid model significantly improves the forecast quality,especially for results at a longer forecasting time.In detail,the Kling-Gupta efficiency(KGE)index,for example,increased from ΔKGE=16%at time t+1h to ΔKGE=69%at time t+6 h.Similar results were obtained for other indicators including peak error and volume error.The computer program developed for this study is being used in practice at the Krong H'nang hydropower to aid in reservoir planning,flood control,and water resource efficiency.
文摘<strong>Introduction:</strong> More than a million people are reported to have been infected with COVID in India, since the beginning of the pandemic. However, the epidemic is not the same across the country. Though there are state-level variations rapidly changing disease dynamics and the response has created uncertainty towards appropriate use of models to project for the future. <strong>Method:</strong> This paper aims at using a validated semi-mechanistic stochastic model to generate short term forecasts. This analysis used data available at the respective state government bulletins for four states. The analysis used a simplified transmission model using Markov Chain Monte Carlo simulation with Metropolis-Hastings updating. <strong>Results:</strong> Two weeks were used to compare the results with the actual data. The forecasted results are well within the 25<sup>th</sup> and 75<sup>th</sup> percentile of the actual cases reported by the respective states. The results indicate a reliable method for a real-time short term forecasting of COVID-19 cases. The 1st week projected interquartile range and actual;reported cases for the state of Kerala, Tamil Nadu, Andhra Pradesh and Odisha were (1064 - 2532) 2234, (17,503 - 50,125) 27,214, (5225 - 11,003) 9563, (2559 - 4461) 3925, respectively. Similarly, the 2<sup>nd</sup> week projected interquartile range and actual;reported cases were (1055 - 7803) 4221, (18,298 - 73,952) 31,488, (4705 - 23,224) 13,357, (2701 - 9037) 4175 respectively. <strong>Conclusion:</strong> This real-time forecast can be used as an early warning tool for projecting the changes in the epidemic in the near future triggering proactive management steps.
基金National Natural Science Foundation of China(No.61701104)the “13th Five Year Plan” Research Foundation of Jilin Provincial Department of Education,China(No.JJKH2017018KJ)
文摘Real-time electricity price(RTEP)influence factor extraction is essential to forecasting accurate power system electricity prices.At present,new electricity price forecasting models have been studied to improve predictive accuracy,ignoring the extraction and analysis of RTEP influence factors.In this study,a correlation analysis method is proposed based on stochastic matrix theory.Firstly,an augmented matrix is formulated,including RTEP influence factor data and RTEP state data.Secondly,data correlation analysis results are obtained given the statistical characteristics of source data based on stochastic matrix theory.Mean spectral radius(MSR)is used as the measure of correlativity.Finally,the proposed method is evaluated in New England electricity markets and compared with the BP neural network forecasting method.Experimental results show that the extracted index system comprehensively generalizes RTEP influence factors,which play a significant role in improving RTEP forecasting accuracy.
文摘Today,urban traffic,growing populations,and dense transportation networks are contributing to an increase in traffic incidents.These incidents include traffic accidents,vehicle breakdowns,fires,and traffic disputes,resulting in long waiting times,high carbon emissions,and other undesirable situations.It is vital to estimate incident response times quickly and accurately after traffic incidents occur for the success of incident-related planning and response activities.This study presents a model for forecasting the traffic incident duration of traffic events with high precision.The proposed model goes through a 4-stage process using various features to predict the duration of four different traffic events and presents a feature reduction approach to enable real-time data collection and prediction.In the first stage,the dataset consisting of 24,431 data points and 75 variables is prepared by data collection,merging,missing data processing and data cleaning.In the second stage,models such as Decision Trees(DT),K-Nearest Neighbour(KNN),Random Forest(RF)and Support Vector Machines(SVM)are used and hyperparameter optimisation is performed with GridSearchCV.In the third stage,feature selection and reduction are performed and real-time data are used.In the last stage,model performance with 14 variables is evaluated with metrics such as accuracy,precision,recall,F1-score,MCC,confusion matrix and SHAP.The RF model outperforms other models with an accuracy of 98.5%.The study’s prediction results demonstrate that the proposed dynamic prediction model can achieve a high level of success.
基金National Natural Science Foundation of China(41475060,41275067,41405060)
文摘Using real-time correction technology for typhoons, this paper discusses real-time correction for forecasting the track of four typhoons during 2009 and 2010 in Japan, Beijing, Guangzhou, and Shanghai. It was determined that the short-time forecast effect was better than the original objective mode. By selecting four types of integration schemes after multiple mode path integration for those four objective modes, the forecast effect of the multi-mode path integration is better, on average, than any single model. Moreover, multi-mode ensemble forecasting has obvious advantages during the initial 36 h.
文摘This paper reports the use of a specialized, mesoscale, numerical weather prediction （NWP） system and a satellite imaging and prediction system that were set up to support the CLAMS （Chesapeake Lighthouse and Aircraft Measurements for Satellites） field campaign during the summer of 2001. The primary objective of CLAMS was to validate satellite-based retrievals of aerosol properties and vertical profiles of the radiative flux, temperature and water vapor. Six research aircraft were deployed to make detailed coincident measurements of the atmosphere and ocean surface with the research satellites that orbited overhead. The mesoscale weather modeling system runs in real-time to provide high spatial and temporal resolution for forecasts that are delivered via the World Wide Web along with a variety of satellite imagery and satellite location predictions. This system is a multi-purpose modeling system capable of both data analysis/assimilation and multi-scale NWP ranging from cloud-scale to larger than regional scale. This is a three-dimensional, non-hydrostatic compressible model in a terrain-following coordinate. The model employs advanced numerical techniques and contains detailed interactive physical processes. The utility of the forecasting system is illustrated throughout the discussion on the impact of the surface-wind forecast on BRDF （Bidirectional Reflectance Distribution Function） and the description of the cloud/moisture forecast versus the aircraft measurement.
文摘Ocean Renewable Energy(ORE)systems—comprising wind,wave,tidal,and ocean thermal energy—are increasingly seen as viable alternatives to fossil fuels.However,their integration into the power grid is hindered by environmental sensitivity,dynamic ocean conditions,and high maintenance demands.Artificial Intelligence(AI)offers promising solutions to these challenges by enabling intelligent,adaptive,and resilient energy systems.This review explores AI applications in ORE,focusing on three critical domains:optimization,forecasting,and control.Optimization techniques,including Genetic Algorithms(GA)and Swarm Intelligence(SI),are employed to enhance device efficiency,improve energy capture,optimize farm layouts,reduce environmental impacts,and lower installation costs.Forecasting uses Machine Learning(ML)and Deep Learning(DL)models to predict wave height,tidal flow,and energy output,aiding in grid integration and energy scheduling.In control systems,AI approaches like Reinforcement Learning(RL)and Fuzzy Logic ensure real-time responsiveness and predictive maintenance,improving system reliability in dynamic marine environments.Emerging technologies such as Edge AI enable decentralized computation for real-time decision-making,while Digital Twin frameworks simulate and predict system performance before deployment.Explainable AI(XAI)is also discussed to ensure transparent and trustworthy decision-making.Ethical and regulatory concerns are acknowledged to ensure responsible AI integration in ocean settings.Overall this review offers a comprehensive synthesis of how AI enhances the performance,efficiency,and scalability of ORE systems.It serves as a valuable resource for researchers,policymakers,and industry professionals seeking to advance clean,smart,and sustainable ocean energy solutions.
文摘Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation.This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies:Hybrid-Si,Mono-Si,and Poly-Si,across three forecasting horizons:1-step,12-step,and 24-step.Among the tested models,the Convolutional Neural Network—Long Short-Term Memory(CNN-LSTM)architecture exhibited superior performance,particularly for the 24-step horizon,achieving R^(2)=0.9793 and MAE 0.0162 for the Poly-Si array,followed by Mono-Si(R^(2)=0.9768)and Hybrid-Si arrays(R^(2)=0.9769).These findings demonstrate that the CNN-LSTM model can provide accurate and reliable PV power predictions for all studied technologies.By identifying the most suitable predictive model for each panel technology,this study contributes to optimizing PV power forecasting and improving energy management strategies.
基金supported by the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20230685)the National Science Foundation of China(Grant No.42277161).
文摘Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predictability,deep learning has yet to be sufficiently explored for complex deformation patterns associated with landslides and is inherently opaque.Herein,we developed a holistic landslide deformation forecasting method that considers spatiotemporal correlations of landslide deformation by integrating domain knowledge into interpretable deep learning.By spatially capturing the interconnections between multiple deformations from different observation points,our method contributes to the understanding and forecasting of landslide systematic behavior.By integrating specific domain knowledge relevant to each observation point and merging internal properties with external variables,the local heterogeneity is considered in our method,identifying deformation temporal patterns in different landslide zones.Case studies involving reservoir-induced landslides and creeping landslides demonstrated that our approach(1)enhances the accuracy of landslide deformation forecasting,(2)identifies significant contributing factors and their influence on spatiotemporal deformation characteristics,and(3)demonstrates how identifying these factors and patterns facilitates landslide forecasting.Our research offers a promising and pragmatic pathway toward a deeper understanding and forecasting of complex landslide behaviors.
基金funded by the ICT Division of theMinistry of Posts,Telecommunications,and Information Technology of Bangladesh under Grant Number 56.00.0000.052.33.005.21-7(Tracking No.22FS15306)support from the University of Rajshahi.
文摘The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for healthcare systems,particularly for identifying actions critical to patient well-being.However,challenges such as high computational demands,low accuracy,and limited adaptability persist in Human Motion Recognition(HMR).While some studies have integrated HMR with IoT for real-time healthcare applications,limited research has focused on recognizing MRHA as essential for effective patient monitoring.This study proposes a novel HMR method tailored for MRHA detection,leveraging multi-stage deep learning techniques integrated with IoT.The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions(MBConv)blocks,followed by Convolutional Long Short Term Memory(ConvLSTM)to capture spatio-temporal patterns.A classification module with global average pooling,a fully connected layer,and a dropout layer generates the final predictions.The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets,focusing on MRHA such as sneezing,falling,walking,sitting,etc.It achieves 94.85%accuracy for cross-subject evaluations and 96.45%for cross-view evaluations on NTU RGB+D 120,along with 89.22%accuracy on HMDB51.Additionally,the system integrates IoT capabilities using a Raspberry Pi and GSM module,delivering real-time alerts via Twilios SMS service to caregivers and patients.This scalable and efficient solution bridges the gap between HMR and IoT,advancing patient monitoring,improving healthcare outcomes,and reducing costs.
文摘Accurate energy demand forecasting is crucial in today’s rapidly electrifying world with decentralized systems and integrated renewables.Traditional models struggle with the dynamic complexities,but AI(artificial intelligence),particularly ML(machine learning)and DL(deep learning),offers transformative solutions.This article explores how AI enhances forecasting accuracy,enables real-time adaptability,and supports strategic energy management.It examines the synergy between AI,IoT(Internet of Things)devices,and smart grids in generating predictive and prescriptive insights.Through case studies,we analyze the benefits and challenges of deploying AI in this domain,including data quality,model explainability,and infrastructure needs.Ultimately,AI emerges as a key enabler for the resilient,data-driven energy systems required to meet modern society’s evolving demands and achieve a sustainable future.
