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On-line forecasting model for zinc output based on self-tuning support vector regression and its application
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作者 胡志坤 桂卫华 彭小奇 《Journal of Central South University of Technology》 2004年第4期461-464,共4页
An on-line forecasting model based on self-tuning support vectors regression for zinc output was put forward to maximize zinc output by adjusting operational parameters in the process of imperial smelting furnace. In ... An on-line forecasting model based on self-tuning support vectors regression for zinc output was put forward to maximize zinc output by adjusting operational parameters in the process of imperial smelting furnace. In this model, the mathematical model of support vector regression was converted into the same format as support vector machine for classification. Then a simplified sequential minimal optimization for classification was applied to train the regression coefficient vector α- α* and threshold b. Sequentially penalty parameter C was tuned dynamically through forecasting result during the training process. Finally, an on-line forecasting algorithm for zinc output was proposed. The simulation result shows that in spite of a relatively small industrial data set, the effective error is less than 10% with a remarkable performance of real time. The model was applied to the optimization operation and fault diagnosis system for imperial smelting furnace. 展开更多
关键词 imperial smelting furnace support vectors regression sequential minimal optimization zinc output on-line forecasting
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Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models 被引量:1
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作者 Lu LI Yongjiu DAI +5 位作者 Zhongwang WEI Wei SHANGGUAN Nan WEI Yonggen ZHANG Qingliang LI Xian-Xiang LI 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第7期1326-1341,共16页
Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient... Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes.In addition to PB models,deep learning(DL)models have been widely used in SM predictions recently.However,few pure DL models have notably high success rates due to lacking physical information.Thus,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions.To this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM predictions.The performances of the proposed hybrid models were investigated and compared with two existing hybrid models.The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models.Moreover,the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions.It is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day predictions.These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions. 展开更多
关键词 soil moisture forecasting hybrid model deep learning ConvLSTM attention mechanism
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Comparison among the UECM Model, and the Composite Model in Forecasting Malaysian Imports
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作者 Mohamed A. H. Milad Hanan Moh. B. Duzan 《Open Journal of Statistics》 2024年第2期163-178,共16页
For more than a century, forecasting models have been crucial in a variety of fields. Models can offer the most accurate forecasting outcomes if error terms are normally distributed. Finding a good statistical model f... For more than a century, forecasting models have been crucial in a variety of fields. Models can offer the most accurate forecasting outcomes if error terms are normally distributed. Finding a good statistical model for time series predicting imports in Malaysia is the main target of this study. The decision made during this study mostly addresses the unrestricted error correction model (UECM), and composite model (Combined regression—ARIMA). The imports of Malaysia from the first quarter of 1991 to the third quarter of 2022 are employed in this study’s quarterly time series data. The forecasting outcomes of the current study demonstrated that the composite model offered more probabilistic data, which improved forecasting the volume of Malaysia’s imports. The composite model, and the UECM model in this study are linear models based on responses to Malaysia’s imports. Future studies might compare the performance of linear and nonlinear models in forecasting. 展开更多
关键词 Composite model UECM ARIMA forecasting MALAYSIA
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Fortified Financial Forecasting Models Based on Non-Linear Searching Approaches
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作者 Mohammad R. Hamidizadeh Mohammad E. Fadaeinejad 《Journal of Modern Accounting and Auditing》 2012年第2期232-240,共9页
The paper's aim is how to forecast data with variations involving at times series data to get the best forecasting model. When researchers are going to forecast data with variations involving at times series data (i... The paper's aim is how to forecast data with variations involving at times series data to get the best forecasting model. When researchers are going to forecast data with variations involving at times series data (i.e., secular trends, cyclical variations, seasonal effects, and stochastic variations), they believe the best forecasting model is the one which realistically considers the underlying causal factors in a situational relationship and therefore has the best "track records" in generating data. Paper's models can be adjusted for variations in related a time series which processes a great deal of randomness, to improve the accuracy of the financial forecasts. Because of Na'fve forecasting models are based on an extrapolation of past values for future. These models may be adjusted for seasonal, secular, and cyclical trends in related data. When a data series processes a great deal of randomness, smoothing techniques, such as moving averages and exponential smoothing, may improve the accuracy of the financial forecasts. But neither Na'fve models nor smoothing techniques are capable of identifying major future changes in the direction of a situational data series. Hereby, nonlinear techniques, like direct and sequential search approaches, overcome those shortcomings can be used. The methodology which we have used is based on inferential analysis. To build the models to identify the major future changes in the direction of a situational data series, a comparative model building is applied. Hereby, the paper suggests using some of the nonlinear techniques, like direct and sequential search approaches, to reduce the technical shortcomings. The final result of the paper is to manipulate, to prepare, and to integrate heuristic non-linear searching methods to serve calculating adjusted factors to produce the best forecast data. 展开更多
关键词 Naive forecasting models smoothing techniques Fibonacci and Golden section search line search bycurve fit
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Artificial Intelligence Based Meteorological Parameter Forecasting for Optimizing Response of Nuclear Emergency Decision Support System
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作者 BILAL Ahmed Khan HASEEB ur Rehman +5 位作者 QAISAR Nadeem MUHAMMAD Ahmad Naveed Qureshi JAWARIA Ahad MUHAMMAD Naveed Akhtar AMJAD Farooq MASROOR Ahmad 《原子能科学技术》 EI CAS CSCD 北大核心 2024年第10期2068-2076,共9页
This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weat... This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies. 展开更多
关键词 prediction of meteorological parameters weather research and forecasting model artificial neural networks nuclear emergency support system
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A Hybrid Model Evaluation Based on PCA Regression Schemes Applied to Seasonal Precipitation Forecast
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作者 Pedro M. González-Jardines Aleida Rosquete-Estévez +1 位作者 Maibys Sierra-Lorenzo Arnoldo Bezanilla-Morlot 《Atmospheric and Climate Sciences》 2024年第3期328-353,共26页
Possible changes in the structure and seasonal variability of the subtropical ridge may lead to changes in the rainfall’s variability modes over Caribbean region. This generates additional difficulties around water r... Possible changes in the structure and seasonal variability of the subtropical ridge may lead to changes in the rainfall’s variability modes over Caribbean region. This generates additional difficulties around water resource planning, therefore, obtaining seasonal prediction models that allow these variations to be characterized in detail, it’s a concern, specially for island states. This research proposes the construction of statistical-dynamic models based on PCA regression methods. It is used as predictand the monthly precipitation accumulated, while the predictors (6) are extracted from the ECMWF-SEAS5 ensemble mean forecasts with a lag of one month with respect to the target month. In the construction of the models, two sequential training schemes are evaluated, obtaining that only the shorter preserves the seasonal characteristics of the predictand. The evaluation metrics used, where cell-point and dichotomous methodologies are combined, suggest that the predictors related to sea surface temperatures do not adequately represent the seasonal variability of the predictand, however, others such as the temperature at 850 hPa and the Outgoing Longwave Radiation are represented with a good approximation regardless of the model chosen. In this sense, the models built with the nearest neighbor methodology were the most efficient. Using the individual models with the best results, an ensemble is built that allows improving the individual skill of the models selected as members by correcting the underestimation of precipitation in the dynamic model during the wet season, although problems of overestimation persist for thresholds lower than 50 mm. 展开更多
关键词 Seasonal forecast Principal Component Regression Statistical-Dynamic models
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Statistical Time Series Forecasting Models for Pandemic Prediction
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作者 Ahmed ElShafee Walid El-Shafai +2 位作者 Abeer D.Algarni Naglaa F.Soliman Moustafa H.Aly 《Computer Systems Science & Engineering》 SCIE EI 2023年第10期349-374,共26页
COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease progression.In this case,COVID-19 data is a time-series dataset that can be... COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease progression.In this case,COVID-19 data is a time-series dataset that can be projected using different methodologies.Thus,this work aims to gauge the spread of the outbreak severity over time.Furthermore,data analytics and Machine Learning(ML)techniques are employed to gain a broader understanding of virus infections.We have simulated,adjusted,and fitted several statistical time-series forecasting models,linearML models,and nonlinear ML models.Examples of these models are Logistic Regression,Lasso,Ridge,ElasticNet,Huber Regressor,Lasso Lars,Passive Aggressive Regressor,K-Neighbors Regressor,Decision Tree Regressor,Extra Trees Regressor,Support Vector Regressions(SVR),AdaBoost Regressor,Random Forest Regressor,Bagging Regressor,AuoRegression,MovingAverage,Gradient Boosting Regressor,Autoregressive Moving Average(ARMA),Auto-Regressive Integrated Moving Averages(ARIMA),SimpleExpSmoothing,Exponential Smoothing,Holt-Winters,Simple Moving Average,Weighted Moving Average,Croston,and naive Bayes.Furthermore,our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods.A third stage in the proposed system is to examine three different implementations to determine which model delivers the best performance.Then,this best method is used for future forecasts,and consequently,we can collect the most accurate and dependable predictions. 展开更多
关键词 forecasting COVID-19 predictive models medical viruses mathematical model market research DISEASES
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A model‑free approach to do long‑term volatility forecasting and its variants
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作者 Kejin Wu Sayar Karmakar 《Financial Innovation》 2023年第1期1595-1632,共38页
Volatility forecasting is important in financial econometrics and is mainly based on the application of various GARCH-type models.However,it is difficult to choose a specific GARCH model that works uniformly well acro... Volatility forecasting is important in financial econometrics and is mainly based on the application of various GARCH-type models.However,it is difficult to choose a specific GARCH model that works uniformly well across datasets,and the traditional methods are unstable when dealing with highly volatile or short-sized datasets.The newly pro-posed normalizing and variance stabilizing(NoVaS)method is a more robust and accu-rate prediction technique that can help with such datasets.This model-free method was originally developed by taking advantage of an inverse transformation based on the frame of the ARCH model.In this study,we conduct extensive empirical and simu-lation analyses to investigate whether it provides higher-quality long-term volatility forecasting than standard GARCH models.Specifically,we found this advantage to be more prominent with short and volatile data.Next,we propose a variant of the NoVaS method that possesses a more complete form and generally outperforms the current state-of-the-art NoVaS method.The uniformly superior performance of NoVaS-type methods encourages their wide application in volatility forecasting.Our analyses also highlight the flexibility of the NoVaS idea that allows the exploration of other model structures to improve existing models or solve specific prediction problems. 展开更多
关键词 ARCH-GARCH model free Aggregated forecasting
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Flood Forecasting and Warning System: A Survey of Models and Their Applications in West Africa
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作者 Mohamed Fofana Julien Adounkpe +5 位作者 Sam-Quarco Dotse Hamadoun Bokar Andrew Manoba Limantol Jean Hounkpe Isaac Larbi Adama Toure 《American Journal of Climate Change》 2023年第1期1-20,共20页
Flood events occurrences and frequencies in the world are of immense worry for the stability of the economy and life safety. Africa continent is the third continent the most negatively affected by the flood events aft... Flood events occurrences and frequencies in the world are of immense worry for the stability of the economy and life safety. Africa continent is the third continent the most negatively affected by the flood events after Asia and Europe. Eastern Africa is the most hit in Africa. However, Africa continent is at the early stage in term of flood forecasting models development and implementation. Very few hydrological models for flood forecasting are available and implemented in Africa for the flood mitigation. And for the majority of the cases, they need to be improved because of the time evolution. Flash flood in Bamako (Mali) has been putting both human life and the economy in jeopardy. Studying this phenomenon, as to propose applicable solutions for its alleviation in Bamako is a great concern. Therefore, it is of upmost importance to know the existing scientific works related to this situation in Mali and elsewhere. The main aim was to point out the various solutions implemented by various local and international institutions, in order to fight against the flood events. Two types of methods are used for the flood events adaptation: the structural and non-structural methods. The structural methods are essentially based on the implementation of the structures like the dams, dykes, levees, etc. The problem of these methods is that they may reduce the volume of water that will inundate the area but are not efficient for the prediction of the coming floods and cannot alert the population with any lead time in advance. The non-structural methods are the one allowing to perform the prediction with acceptable lead time. They used the hydrological rainfall-runoff models and are the widely methods used for the flood adaptation. This review is more accentuated on the various types non-structural methods and their application in African countries in general and West African countries in particular with their strengths and weaknesses. Hydrologiska Byråns Vattenbalansavdelning (HBV), Hydrologic Engineer Center Hydrologic Model System (HEC-HMS) and Soil and Water Assessment Tool (SWAT) are the hydrological models that are the most widely used in West Africa for the purpose of flood forecasting. The easily way of calibration and the weak number of input data make these models appropriate for the West Africa region where the data are scarce and often with bad quality. These models when implemented and applied, can predict the coming floods, allow the population to adapt and mitigate the flood events and reduce considerably the impacts of floods especially in terms of loss of life. 展开更多
关键词 Flood forecasting Hydrological models Climate Change WEST
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SeisGuard: A Software Platform to Establish Automatically an Earthquake Forecasting Model
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作者 Xiliang Liu Yajing Gao Mei Li 《Open Journal of Earthquake Research》 2023年第4期177-197,共21页
SeisGuard, a system for analyzing earthquake precursory data, is a software platform to search for earthquake precursory information by processing geophysical data from different sources to establish automatically an ... SeisGuard, a system for analyzing earthquake precursory data, is a software platform to search for earthquake precursory information by processing geophysical data from different sources to establish automatically an earthquake forecasting model. The main function of this system is to analyze and process the deformation, fluid, electromagnetic and other geophysical field observing data from ground-based observation, as well as space-based observation. Combined station and earthquake distributions, geological structure and other information, this system can provide a basic software platform for earthquake forecasting research based on spatiotemporal fusion. The hierarchical station tree for data sifting and the interaction mode have been innovatively developed in this SeisGuard system to improve users’ working efficiency. The data storage framework designed according to the characteristics of different time series can unify the interfaces of different data sources, provide the support of data flow, simplify the management and usage of data, and provide foundation for analysis of big data. The final aim of this development is to establish an effective earthquake forecasting model combined all available information from ground-based observations to space-based observations. 展开更多
关键词 SeisGuard Platform Geophysical Observing Data Electromagnetic Emission Time Series Database Spatiotemporal Fusion Earthquake forecasting model
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TIME SERIES NEURAL NETWORK MODEL FOR HYDROLOGIC FORECASTING 被引量:4
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作者 钟登华 刘东海 Mittnik Stefan 《Transactions of Tianjin University》 EI CAS 2001年第3期182-186,共5页
Time series analysis plays an important role in hydrologic forecasting,while the key to this analysis is to establish a proper model.This paper presents a time series neural network model with back propagation proced... Time series analysis plays an important role in hydrologic forecasting,while the key to this analysis is to establish a proper model.This paper presents a time series neural network model with back propagation procedure for hydrologic forecasting.Free from the disadvantages of previous models,the model can be parallel to operate information flexibly and rapidly.It excels in the ability of nonlinear mapping and can learn and adjust by itself,which gives the model a possibility to describe the complex nonlinear hydrologic process.By using directly a training process based on a set of previous data, the model can forecast the time series of stream flow.Moreover,two practical examples were used to test the performance of the time series neural network model.Results confirm that the model is efficient and feasible. 展开更多
关键词 hydrologic forecasting time series neural network model back propagation
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River channel flood forecasting method of coupling wavelet neural network with autoregressive model 被引量:1
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作者 李致家 周轶 马振坤 《Journal of Southeast University(English Edition)》 EI CAS 2008年第1期90-94,共5页
Based on analyzing the limitations of the commonly used back-propagation neural network (BPNN), a wavelet neural network (WNN) is adopted as the nonlinear river channel flood forecasting method replacing the BPNN.... Based on analyzing the limitations of the commonly used back-propagation neural network (BPNN), a wavelet neural network (WNN) is adopted as the nonlinear river channel flood forecasting method replacing the BPNN. The WNN has the characteristics of fast convergence and improved capability of nonlinear approximation. For the purpose of adapting the timevarying characteristics of flood routing, the WNN is coupled with an AR real-time correction model. The AR model is utilized to calculate the forecast error. The coefficients of the AR real-time correction model are dynamically updated by an adaptive fading factor recursive least square(RLS) method. The application of the flood forecasting method in the cross section of Xijiang River at Gaoyao shows its effectiveness. 展开更多
关键词 river channel flood forecasting wavel'et neural network autoregressive model recursive least square( RLS) adaptive fading factor
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The Application of ARIMA Model in Forecasting of PDSI in Henan Province
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作者 厉玉昇 《Agricultural Science & Technology》 CAS 2016年第3期760-764,共5页
[Objective] The aim was to establish drought forecasting model with high precision. [Method] With an ARIMA regression model, the research performed Palmer Drought mode(PDSI) time series modeling analysis of Henan Pr... [Objective] The aim was to establish drought forecasting model with high precision. [Method] With an ARIMA regression model, the research performed Palmer Drought mode(PDSI) time series modeling analysis of Henan Province based on PDSI time series and DPS(Data Processing Software) in order to build drought forecasting model. [Result] It is feasible to perform drought forecasting with appropriate parameters. [Conclusion] ARIMA model is practical and more precise in PDSI-based drought analysis and forecasting. 展开更多
关键词 ARIMA model PDSI forecasting APPLICATION Henan Province
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Short Term Load Forecasting Using Subset Threshold Auto Regressive Model
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作者 孙海健 《Journal of Southeast University(English Edition)》 EI CAS 1999年第2期78-83,共6页
The subset threshold auto regressive (SSTAR) model, which is capable of reproducing the limit cycle behavior of nonlinear time series, is introduced. The algorithm for fitting the sampled data with SSTAR model is pr... The subset threshold auto regressive (SSTAR) model, which is capable of reproducing the limit cycle behavior of nonlinear time series, is introduced. The algorithm for fitting the sampled data with SSTAR model is proposed and applied to model and forecast power load. Numerical example verifies that desirable accuracy of short term load forecasting can be achieved by using the SSTAR model. 展开更多
关键词 power load forecasting subset threshold auto regressive model
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Load-forecasting method for IES based on LSTM and dynamic similar days with multi-features 被引量:2
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作者 Fan Sun Yaojia Huo +3 位作者 Lei Fu Huilan Liu Xi Wang Yiming Ma 《Global Energy Interconnection》 EI CSCD 2023年第3期285-296,共12页
To fully exploit the rich characteristic variation laws of an integrated energy system(IES)and further improve the short-term load-forecasting accuracy,a load-forecasting method is proposed for an IES based on LSTM an... To fully exploit the rich characteristic variation laws of an integrated energy system(IES)and further improve the short-term load-forecasting accuracy,a load-forecasting method is proposed for an IES based on LSTM and dynamic similar days with multi-features.Feature expansion was performed to construct a comprehensive load day covering the load and meteorological information with coarse and fine time granularity,far and near time periods.The Gaussian mixture model(GMM)was used to divide the scene of the comprehensive load day,and gray correlation analysis was used to match the scene with the coarse time granularity characteristics of the day to be forecasted.Five typical days with the highest correlation with the day to be predicted in the scene were selected to construct a“dynamic similar day”by weighting.The key features of adjacent days and dynamic similar days were used to forecast multi-loads with fine time granularity using LSTM.Comparing the static features as input and the selection method of similar days based on non-extended single features,the effectiveness of the proposed prediction method was verified. 展开更多
关键词 Integrated energy system Load forecast Long short-term memory Dynamic similar days Gaussian mixture model
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A data assimilation-based forecast model of outer radiation belt electron fluxes 被引量:2
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作者 Yuan Lei Xing Cao +3 位作者 BinBin Ni Song Fu TaoRong Luo XiaoYu Wang 《Earth and Planetary Physics》 CAS CSCD 2023年第6期620-630,共11页
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
关键词 Earth’s outer radiation belt data assimilation electron flux forecast model performance evaluation
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CT-NET: A Novel Convolutional Transformer-Based Network for Short-Term Solar Energy Forecasting Using Climatic Information 被引量:1
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作者 Muhammad Munsif Fath U Min Ullah +2 位作者 Samee Ullah Khan Noman Khan Sung Wook Baik 《Computer Systems Science & Engineering》 SCIE EI 2023年第11期1751-1773,共23页
Photovoltaic(PV)systems are environmentally friendly,generate green energy,and receive support from policies and organizations.However,weather fluctuations make large-scale PV power integration and management challeng... Photovoltaic(PV)systems are environmentally friendly,generate green energy,and receive support from policies and organizations.However,weather fluctuations make large-scale PV power integration and management challenging despite the economic benefits.Existing PV forecasting techniques(sequential and convolutional neural networks(CNN))are sensitive to environmental conditions,reducing energy distribution system performance.To handle these issues,this article proposes an efficient,weather-resilient convolutional-transformer-based network(CT-NET)for accurate and efficient PV power forecasting.The network consists of three main modules.First,the acquired PV generation data are forwarded to the pre-processing module for data refinement.Next,to carry out data encoding,a CNNbased multi-head attention(MHA)module is developed in which a single MHA is used to decode the encoded data.The encoder module is mainly composed of 1D convolutional and MHA layers,which extract local as well as contextual features,while the decoder part includes MHA and feedforward layers to generate the final prediction.Finally,the performance of the proposed network is evaluated using standard error metrics,including the mean squared error(MSE),root mean squared error(RMSE),and mean absolute percentage error(MAPE).An ablation study and comparative analysis with several competitive state-of-the-art approaches revealed a lower error rate in terms of MSE(0.0471),RMSE(0.2167),and MAPE(0.6135)over publicly available benchmark data.In addition,it is demonstrated that our proposed model is less complex,with the lowest number of parameters(0.0135 M),size(0.106 MB),and inference time(2 ms/step),suggesting that it is easy to integrate into the smart grid. 展开更多
关键词 Solar energy forecasting renewable energy systems photovoltaic generation forecasting time series data transformer models deep learning machine learning
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The Water-Bearing Numerical Model and Its Operational Forecasting Experiments PartII: The Operational Forecasting Experiments 被引量:19
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作者 徐幼平 夏大庆 钱越英 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 1998年第3期39-54,共16页
おhe water-bearing numerical model is undergone all round examinations during the operational forecasting experiments from 1994 to 1996. A lot of difficult problems arising from the model′s water-bearing are successf... おhe water-bearing numerical model is undergone all round examinations during the operational forecasting experiments from 1994 to 1996. A lot of difficult problems arising from the model′s water-bearing are successfully resolved in these experiments through developing and using a series of technical measures. The operational forecasting running of the water-bearing numerical model is realized stably and reliably, and satisfactory forecasts are obtained. 展开更多
关键词 Water-bearing Numerical forecasting model Operational forecasting experiment
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A GCM-Based Forecasting Model for the Landfall of Tropical Cyclones in China 被引量:8
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作者 孙建奇 Joong Bae AHN 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2011年第5期1049-1055,共7页
A statistical dynamic model for forecasting Chinese landfall of tropical cyclones (CLTCs) was developed based on the empirical relationship between the observed CLTC variability and the hindcast atmospheric circulat... A statistical dynamic model for forecasting Chinese landfall of tropical cyclones (CLTCs) was developed based on the empirical relationship between the observed CLTC variability and the hindcast atmospheric circulations from the Pusan National University coupled general circulation model (PNU-CGCM).In the last 31 years,CLTCs have shown strong year-to-year variability,with a maximum frequency in 1994 and a minimum frequency in 1987.Such features were well forecasted by the model.A cross-validation test showed that the correlation between the observed index and the forecasted CLTC index was high,with a coefficient of 0.71.The relative error percentage (16.3%) and root-mean-square error (1.07) were low.Therefore the coupled model performs well in terms of forecasting CLTCs;the model has potential for dynamic forecasting of landfall of tropical cyclones. 展开更多
关键词 statistical-dynamical model cyclone forecast tropical cyclone coupled model cross validation
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Interval grey number sequence prediction by using non-homogenous exponential discrete grey forecasting model 被引量:19
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作者 Naiming Xie Sifeng Liu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2015年第1期96-102,共7页
This paper aims to study a new grey prediction approach and its solution for forecasting the main system variable whose accurate value could not be collected while the potential value set could be defined. Based on th... This paper aims to study a new grey prediction approach and its solution for forecasting the main system variable whose accurate value could not be collected while the potential value set could be defined. Based on the traditional nonhomogenous discrete grey forecasting model(NDGM), the interval grey number and its algebra operations are redefined and combined with the NDGM model to construct a new interval grey number sequence prediction approach. The solving principle of the model is analyzed, the new accuracy evaluation indices, i.e. mean absolute percentage error of mean value sequence(MAPEM) and mean percent of interval sequence simulating value set covered(MPSVSC), are defined and, the procedure of the interval grey number sequence based the NDGM(IG-NDGM) is given out. Finally, a numerical case is used to test the modelling accuracy of the proposed model. Results show that the proposed approach could solve the interval grey number sequence prediction problem and it is much better than the traditional DGM(1,1) model and GM(1,1) model. 展开更多
关键词 grey number grey system theory INTERVAL discrete grey forecasting model non-homogeneous exponential sequence
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