scholarly journals A Method of Ground-Based Cloud Motion Predict: CCLSTM + SR-Net

2021 ◽  
Vol 13 (19) ◽  
pp. 3876
Author(s):  
Zhiying Lu ◽  
Zehan Wang ◽  
Xin Li ◽  
Jianfeng Zhang
Keyword(s):  
Short Term Memory ◽  
Morphological Changes ◽  
Motion Vector ◽  
Prediction Method ◽  
Super Resolution ◽  
Motion Prediction ◽  
Short Term ◽  
Cloud Motion ◽  
Atmospheric Radiation Measurement ◽  
Long Short Term Memory

Ground-based cloud images can provide information on weather and cloud conditions, which play an important role in cloud cover monitoring and photovoltaic power generation forecasting. However, the cloud motion prediction of ground-based cloud images still lacks advanced and complete methods, and traditional technologies based on image processing and motion vector calculation are difficult to predict cloud morphological changes. In this paper, we propose a cloud motion prediction method based on Cascade Causal Long Short-Term Memory (CCLSTM) and Super-Resolution Network (SR-Net). Firstly, CCLSTM is used to estimate the shape and speed of cloud motion. Secondly, the Super-Resolution Network is built based on perceptual losses to reconstruct the result of CCLSTM and, finally, make it clearer. We tested our method on Atmospheric Radiation Measurement (ARM) Climate Research Facility TSI (total sky imager) images. The experiments showed that the method is able to predict the sky cloud changes in the next few steps.

scholarly journals Interval prediction method based on Long-Short Term Memory networks for system integrated of hydro, wind and solar power

2019 ◽  
Vol 158 ◽  
pp. 6176-6182 ◽  
Author(s):  
Zhendong Zhang ◽  
Hui Qin ◽  
Liqiang Yao ◽  
Jiantao Lu ◽  
Liangge Cheng
Keyword(s):  
Solar Power ◽  
Short Term Memory ◽  
Prediction Method ◽  
Short Term ◽  
Term Memory ◽  
Interval Prediction ◽  
Long Short Term Memory

scholarly journals An AutoEncoder and LSTM-Based Traffic Flow Prediction Method

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2946 ◽  
Author(s):  
Wangyang Wei ◽  
Honghai Wu ◽  
Huadong Ma
Keyword(s):  
Traffic Flow ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Smart Cities ◽  
Prediction Method ◽  
Short Term ◽  
Term Memory ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Long Short Term Memory

Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

Human-Like Hand Reaching by Motion Prediction Using Long Short-Term Memory

2017 ◽  
pp. 156-166 ◽  
Author(s):  
Phongtharin Vinayavekhin ◽  
Michiaki Tatsubori ◽  
Daiki Kimura ◽  
Yifan Huang ◽  
Giovanni De Magistris ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Motion Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Cubic LSTMs for Video Prediction

2019 ◽  
Vol 33 ◽  
pp. 8263-8270 ◽  
Author(s):  
Hehe Fan ◽  
Linchao Zhu ◽  
Yi Yang
Keyword(s):  
Learning Communities ◽  
Moving Objects ◽  
Short Term Memory ◽  
Robot Learning ◽  
Motion Prediction ◽  
Short Term ◽  
The Core ◽  
Long Short Term Memory ◽  
Real World Datasets ◽  
Video Prediction

Predicting future frames in videos has become a promising direction of research for both computer vision and robot learning communities. The core of this problem involves moving object capture and future motion prediction. While object capture specifies which objects are moving in videos, motion prediction describes their future dynamics. Motivated by this analysis, we propose a Cubic Long Short-Term Memory (CubicLSTM) unit for video prediction. CubicLSTM consists of three branches, i.e., a spatial branch for capturing moving objects, a temporal branch for processing motions, and an output branch for combining the first two branches to generate predicted frames. Stacking multiple CubicLSTM units along the spatial branch and output branch, and then evolving along the temporal branch can form a cubic recurrent neural network (CubicRNN). Experiment shows that CubicRNN produces more accurate video predictions than prior methods on both synthetic and real-world datasets.

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