scholarly journals A Correction Method of Environmental Meteorological Model Based on Long‐Short‐Term Memory Neural Network

2019 ◽  
Vol 6 (11) ◽  
pp. 2214-2226 ◽  
Author(s):  
Yuliang Dai ◽  
Zhenyu Lu ◽  
Hengde Zhang ◽  
Tianming Zhan ◽  
Jia Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Correction Method ◽  
Short Term ◽  
Meteorological Model ◽  
Term Memory ◽  
Model Based ◽  
Long Short Term Memory

Enterprise Profit Forecast Model Based on Long Short-Term Memory Neural Network

2021 ◽  
Author(s):  
Zhang Qianyu ◽  
Liu Dongping ◽  
Zhu Xueying ◽  
Chen Huaisen ◽  
Zhou Xiaozhou
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Forecast Model ◽  
Short Term ◽  
Term Memory ◽  
Model Based ◽  
Long Short Term Memory

scholarly journals Bridge Temperature Prediction Model Based on Long-Short Term Memory Neural Network

2021 ◽  
Vol 1966 (1) ◽  
pp. 012013
Author(s):  
Jingxiao Shu ◽  
Dongyue Zhao ◽  
Xuda Zheng ◽  
Yiwen Li ◽  
Yufeng Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Short Term Memory ◽  
Short Term ◽  
Temperature Prediction ◽  
Term Memory ◽  
Model Based ◽  
Long Short Term Memory

scholarly journals Construction of the Open Oral Evaluation Model Based on the Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhixin Chen ◽  
Xu Zhang ◽  
Zhiyuan Li ◽  
Anchu Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Oral Language ◽  
Short Term Memory ◽  
Evaluation Model ◽  
Short Term ◽  
Term Memory ◽  
Scoring Model ◽  
Model Based ◽  
Long Short Term Memory

According to the problem of low efficiency and low scoring accuracy of the traditional oral language scoring system, this study builds an open oral language evaluation model based on the basic principles of deep learning technology. Firstly, the basic methods of the convolutional neural network (CNN) and long short-term memory (LSTM) neural network are introduced. Then, we combine the convolutional neural network (CNN) and long short-term memory (LSTM) neural network to design an open oral scoring model based on CNN + LSTM, which divides the oral evaluation model into the speech scoring model and text scoring model and makes a specific implementation of two scoring models, respectively. An experimental environment is then built to preprocess the data, and finally, the model built in this study is trained and simulated. The experimental results show that the CNN + LSTM network evaluation model has a better comprehensive scoring performance, higher scoring efficiency, and higher accuracy and has feasibility and practicability.

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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