scholarly journals Bus Travel Speed Prediction Using Long Short-term Memory Neural Network

2020 ◽  
Vol 32 (12) ◽  
pp. 4441
Author(s):  
Seung-Bae Jeon ◽  
Myeong-Hun Jeong ◽  
Tae-Young Lee ◽  
Jeong-Hwan Lee ◽  
Jae-Myoung Cho
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Travel Speed ◽  
Short Term ◽  
Term Memory ◽  
Speed Prediction ◽  
Long Short Term Memory

Ego-Vehicle Speed Prediction Using a Long Short-Term Memory Based Recurrent Neural Network

2019 ◽  
Vol 20 (4) ◽  
pp. 713-722 ◽  
Author(s):  
Kyuhwan Yeon ◽  
Kyunghan Min ◽  
Jaewook Shin ◽  
Myoungho Sunwoo ◽  
Manbae Han
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Vehicle Speed ◽  
Short Term ◽  
Term Memory ◽  
Speed Prediction ◽  
Long Short Term Memory

scholarly journals Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

2020 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Dana-Mihaela Petroșanu ◽  
Alexandru Pîrjan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Short Term ◽  
Term Memory ◽  
Storage Room ◽  
Long Short Term Memory ◽  
Commercial Center ◽  
Business And Management

The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.

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