Accurate prediction of parking spaces plays a crucial role in maximizing the efficiency of parking resources and optimizing traffic conditions.However,the majority of earlier research has used models based on past par...Accurate prediction of parking spaces plays a crucial role in maximizing the efficiency of parking resources and optimizing traffic conditions.However,the majority of earlier research has used models based on past parking data or the plethora of variables that influence parking prediction,which not only makes the data more complicated and costs more time to run but can also lead to poor model fits.To solve this problem,a hybrid parking prediction model combining complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and gated recurrent unit(GRU)model is proposed to predict the number of parking spaces.In this model,CEEMDAN has the ability to gradually break down time series fluctuations or trends at various scales,producing a sequence of intrinsic mode functions(IMF)with various characteristic scales.Then,by keeping the majority of the original data’s content,removing superfluous information,and enhancing predicted response time,principal component analysis(PCA)decreases the dimensionality of the IMF series.Subsequently,the high-level abstract characteristics are entered into the GRU network,and the network is built,tested,and predicted based on the deep learning framework Keras.The validity of the presented model is verified by making use of real parking datasets from two three-dimensional parking lots.The test results reveal that the model outperforms the baseline model’s predictive accuracy,i.e.,a lower testing error.The real parking time series are most closely modeled by the CEEMDAN-PCA-GRU model.As a result,the method is superior to existing models for parking prediction.展开更多
Randomness and fluctuations in wind power output may cause changes in important parameters(e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors...Randomness and fluctuations in wind power output may cause changes in important parameters(e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors(such as weather),there are often various anomalies in wind power data,such as missing numerical values and unreasonable data.This significantly affects the accuracy of wind power generation predictions and operational decisions.Therefore,developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry.In this study,the causes of abnormal data in wind power generation were first analyzed from a practical perspective.Second,an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)method with a generative adversarial interpolation network(GAIN)network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components.Finally,a complete wind power generation time series was reconstructed.Compared to traditional methods,the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations.展开更多
Accurate wind speed prediction has been becoming an indispensable technology in system security,wind energy utilization,and power grid dispatching in recent years.However,it is an arduous task to predict wind speed du...Accurate wind speed prediction has been becoming an indispensable technology in system security,wind energy utilization,and power grid dispatching in recent years.However,it is an arduous task to predict wind speed due to its variable and random characteristics.For the objective to enhance the performance of forecasting short-term wind speed,this work puts forward a hybrid deep learning model mixing time series decomposition algorithm and gated recurrent unit(GRU).The time series decomposition algorithm combines the following two parts:(1)the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),and(2)wavelet packet decomposition(WPD).Firstly,the normalized wind speed time series(WSTS)are handled by CEEMDAN to gain pure fixed-frequency components and a residual signal.The WPD algorithm conducts the second-order decomposition to the first component that contains complex and high frequency signal of raw WSTS.Finally,GRU networks are established for all the relevant components of the signals,and the predicted wind speeds are obtained by superimposing the prediction of each component.Results from two case studies,adopting wind data from laboratory and wind farm,respectively,suggest that the related trend of the WSTS can be separated effectively by the proposed time series decomposition algorithm,and the accuracy of short-time wind speed prediction can be heightened significantly mixing the time series decomposition algorithm and GRU networks.展开更多
基金the National Natural Science Foundation of China(No.52062027)the Key Research and Development Project of Gansu Province(No.22YF7GA142)+3 种基金the Soft Science Special Project of Gansu Basic Research Plan(No.22JR4ZA035)the Gansu Provincial Science and Technology Major Special Project-Enterprise Innovation Consortium Project(Nos.22ZD6GA010 and 21ZD3GA002)the Natural Science Foundation of Gansu Province(No.22JR5RA343)the Gansu Provincial Education Technology Innovation Project(No.2023CXZX-582)。
文摘Accurate prediction of parking spaces plays a crucial role in maximizing the efficiency of parking resources and optimizing traffic conditions.However,the majority of earlier research has used models based on past parking data or the plethora of variables that influence parking prediction,which not only makes the data more complicated and costs more time to run but can also lead to poor model fits.To solve this problem,a hybrid parking prediction model combining complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and gated recurrent unit(GRU)model is proposed to predict the number of parking spaces.In this model,CEEMDAN has the ability to gradually break down time series fluctuations or trends at various scales,producing a sequence of intrinsic mode functions(IMF)with various characteristic scales.Then,by keeping the majority of the original data’s content,removing superfluous information,and enhancing predicted response time,principal component analysis(PCA)decreases the dimensionality of the IMF series.Subsequently,the high-level abstract characteristics are entered into the GRU network,and the network is built,tested,and predicted based on the deep learning framework Keras.The validity of the presented model is verified by making use of real parking datasets from two three-dimensional parking lots.The test results reveal that the model outperforms the baseline model’s predictive accuracy,i.e.,a lower testing error.The real parking time series are most closely modeled by the CEEMDAN-PCA-GRU model.As a result,the method is superior to existing models for parking prediction.
基金We gratefully acknowledge the support of National Natural Science Foundation of China(NSFC)(Grant No.51977133&Grant No.U2066209).
文摘Randomness and fluctuations in wind power output may cause changes in important parameters(e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors(such as weather),there are often various anomalies in wind power data,such as missing numerical values and unreasonable data.This significantly affects the accuracy of wind power generation predictions and operational decisions.Therefore,developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry.In this study,the causes of abnormal data in wind power generation were first analyzed from a practical perspective.Second,an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)method with a generative adversarial interpolation network(GAIN)network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components.Finally,a complete wind power generation time series was reconstructed.Compared to traditional methods,the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations.
基金This work was supported in part by the National Key Research and Development Project of China(No.2019YFE0105300)the National Natural Science Foundation of China(No.61972443)the Hunan Provincial Key Research and Development Project of China(No.2022WK2006).
文摘Accurate wind speed prediction has been becoming an indispensable technology in system security,wind energy utilization,and power grid dispatching in recent years.However,it is an arduous task to predict wind speed due to its variable and random characteristics.For the objective to enhance the performance of forecasting short-term wind speed,this work puts forward a hybrid deep learning model mixing time series decomposition algorithm and gated recurrent unit(GRU).The time series decomposition algorithm combines the following two parts:(1)the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),and(2)wavelet packet decomposition(WPD).Firstly,the normalized wind speed time series(WSTS)are handled by CEEMDAN to gain pure fixed-frequency components and a residual signal.The WPD algorithm conducts the second-order decomposition to the first component that contains complex and high frequency signal of raw WSTS.Finally,GRU networks are established for all the relevant components of the signals,and the predicted wind speeds are obtained by superimposing the prediction of each component.Results from two case studies,adopting wind data from laboratory and wind farm,respectively,suggest that the related trend of the WSTS can be separated effectively by the proposed time series decomposition algorithm,and the accuracy of short-time wind speed prediction can be heightened significantly mixing the time series decomposition algorithm and GRU networks.
