scholarly journals Deep tree neural network for multiple‐time‐step prediction of short‐term speed and confidence estimation

2021 ◽  
Vol 15 (3) ◽  
pp. 446-462
Author(s):  
Yanyun Tao ◽  
Xiang Wang ◽  
Jianying Zheng ◽  
Wenjuan E ◽  
Po Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Multiple Time ◽  
Short Term ◽  
Confidence Estimation ◽  
Time Step ◽  
Multiple Time Step

scholarly journals Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3517 ◽  
Author(s):  
Anh Ngoc-Lan Huynh ◽  
Ravinesh C. Deo ◽  
Duc-Anh An-Vo ◽  
Mumtaz Ali ◽  
Nawin Raj ◽  
...  
Keyword(s):  
Deep Learning ◽  
Solar Radiation ◽  
Short Term Memory ◽  
Global Solar Radiation ◽  
Multiple Time ◽  
Short Term ◽  
Time Step ◽  
Term Memory ◽  
Multiple Time Step ◽  
Long Short Term Memory

This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy).

k-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition

2016 ◽  
Vol 142 (6) ◽  
pp. 04016018 ◽  
Author(s):  
Bin Yu ◽  
Xiaolin Song ◽  
Feng Guan ◽  
Zhiming Yang ◽  
Baozhen Yao
Keyword(s):  
Nearest Neighbor ◽  
Multiple Time ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Time Step ◽  
Traffic Condition ◽  
Multiple Time Step

Short-Term Traffic Volume Forecast Method Based On CNN-LSTM-At

2021 ◽  
Author(s):  
Xing Xu ◽  
Chengxing Liu ◽  
Yun Zhao ◽  
Xuyang Yu ◽  
Xiang Wu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Forecast Model ◽  
Traffic Volume ◽  
Model Verification ◽  
Short Term ◽  
Time Step ◽  
Sequence Prediction ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Abstract In order to tackle existing traffic flow prediction problem, a Traffic Volume Forecast Model based on deep learning is designed. The model implements Convolutional Neural Network (CNN) to extract spatial matrix information, uses long and short-term neural network (LSTM) for sequence prediction, appends attention mechanism to time step on LSTM, and assigns weights to different time steps. By implementing model verification on the Chengdu taxi dataset, dividing data into various categories, cross validating different categories of data, and comparing the model with other models, it is concluded that the CNN-LSTM-At network model proposed in this article has higher accuracy compared with traditional network model.

Dangers of Multiple Time Step Methods

1993 ◽  
Vol 109 (2) ◽  
pp. 318-328 ◽  
Author(s):  
Jeffrey J. Biesiadecki ◽  
Robert D. Skeel
Keyword(s):  
Multiple Time ◽  
Time Step ◽  
Multiple Time Step
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