Well logging prediction and uncertainty analysis based on recurrent neural network with attention mechanism and Bayesian theory

2022 ◽  
Vol 208 ◽  
pp. 109458
Author(s):  
Lili Zeng ◽  
Weijian Ren ◽  
Liqun Shan ◽  
Fengcai Huo
Keyword(s):  
Neural Network ◽  
Uncertainty Analysis ◽  
Recurrent Neural Network ◽  
Well Logging ◽  
Attention Mechanism ◽  
Bayesian Theory

scholarly journals Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network

2019 ◽  
Vol 11 (12) ◽  
pp. 247
Author(s):  
Xin Zhou ◽  
Peixin Dong ◽  
Jianping Xing ◽  
Peijia Sun
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Public Transportation ◽  
Prediction Accuracy ◽  
Arrival Time ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Support Vector ◽  
Data Set ◽  
Dynamic Factors

Accurate prediction of bus arrival times is a challenging problem in the public transportation field. Previous studies have shown that to improve prediction accuracy, more heterogeneous measurements provide better results. So what other factors should be added into the prediction model? Traditional prediction methods mainly use the arrival time and the distance between stations, but do not make full use of dynamic factors such as passenger number, dwell time, bus driving efficiency, etc. We propose a novel approach that takes full advantage of dynamic factors. Our approach is based on a Recurrent Neural Network (RNN). The experimental results indicate that a variety of prediction algorithms (such as Support Vector Machine, Kalman filter, Multilayer Perceptron, and RNN) have significantly improved performance after using dynamic factors. Further, we introduce RNN with an attention mechanism to adaptively select the most relevant input factors. Experiments demonstrate that the prediction accuracy of RNN with an attention mechanism is better than RNN with no attention mechanism when there are heterogeneous input factors. The experimental results show the superior performances of our approach on the data set provided by Jinan Public Transportation Corporation.

Attention mechanism for developing wind speed and solar irradiance forecasting models

2020 ◽  
pp. 0309524X2098188
Author(s):  
Banalaxmi Brahma ◽  
Rajesh Wadhvani ◽  
Sanyam Shukla
Keyword(s):  
Neural Network ◽  
Renewable Energy ◽  
Wind Speed ◽  
Recurrent Neural Network ◽  
Solar Irradiance ◽  
Power Plants ◽  
Energy Resource ◽  
Attention Mechanism ◽  
Wind Speed Prediction ◽  
Forecasting Models

This article presents the Recurrent Neural Network (RNN) and its Attention mechanism to develop forecasting models for renewable energy applications. In this study, wind speed and solar irradiance forecasting models have been developed as these two factors play a significant role in renewable energy production. The irregular nature of wind poses the challenge of accurate wind speed prediction, while solar irradiance forecasting can aid in the planning and deployment of solar power plants. In this paper, six RNN techniques, namely RNN, GRU, LSTM, Content-based Attention, Luong Attention, and Self-Attention based RNN are considered for forecasting the future values of wind speed and solar irradiance in particular geographical locations. The aim is the identification of the advantages, comparison, and importance of different recurrent neural network methods for forecasting models. All models are developed on the datasets of the National Renewable Energy Laboratory (NREL) and NASA’s Prediction of Worldwide Energy Resource (POWER).

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