Long-Term Wind Speed and Power Forecasting Using Local Recurrent Neural Network Models

2006 ◽  
Vol 21 (1) ◽  
pp. 273-284 ◽  
Author(s):  
T.G. Barbounis ◽  
J.B. Theocharis ◽  
M.C. Alexiadis ◽  
P.S. Dokopoulos
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Power Forecasting

scholarly journals Modelling Particulate Matter Using Multivariate and Multistep Recurrent Neural Networks

2021 ◽  
Vol 9 ◽  
Author(s):  
Tushar Saini ◽  
Pratik Chaturvedi ◽  
Varun Dutt
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Economic Losses ◽  
Ensemble Model ◽  
Neural Network Models ◽  
Ensemble Techniques ◽  
Bidirectional Lstm

Air quality is a major problem in the world, having severe health implications. Long-term exposure to poor air quality causes pulmonary and cardiovascular diseases. Several studies have also found that deteriorating air quality also causes substantial economic losses. Thus, techniques that can forecast air quality with higher accuracy may help reduce health and economic consequences. Prior research has utilized state-of-the-art artificial neural network and recurrent neural network models for forecasting air quality. However, a comprehensive investigation of different architectures of recurrent neural network, especially LSTMs and ensemble techniques, has been less explored. Also, there have been less explorations of long-term air quality forecasts via these methods exists. This research proposes the development and calibration of recurrent neural network models and their ensemble, which can forecast air quality in terms of PM2.5 concentration 6 hours ahead in time. For forecasting air quality, a vanilla-LSTM, a stack-LSTM, a bidirectional-LSTM, a CNN-LSTM, and an ensemble of individual LSTM models were trained on the UCI Machine Learning Beijing dataset. Data were split into two parts, where 80% of data were used for training the models, while the remaining 20% were used for validating the models. For comparative analysis, four regression losses were calculated, namely root mean squared error, mean absolute percentage error, mean absolute error and Pearson’s correlation coefficient. Results revealed that among all models, the ensemble model performed the best in predicting the PM2.5 concentrations. Furthermore, the ensemble model outperformed other models reported in literature by a long margin. Among the individual models, the bidirectional-LSTM performed the best. We highlight the implications of this research on long-term forecasting of air quality via recurrent and ensemble techniques.

Convergence Theory for Unified Discrete-Time RNNs with Quasi-Symmetric Connection

2014 ◽  
Vol 538 ◽  
pp. 167-170
Author(s):  
Hui Zhong Mao ◽  
Chen Qiao ◽  
Wen Feng Jing ◽  
Xi Chen ◽  
Jin Qin Mao
Keyword(s):  
Neural Network ◽  
Global Convergence ◽  
Discrete Time ◽  
Recurrent Neural Network ◽  
Symmetric Matrix ◽  
Network Models ◽  
Convergence Theory ◽  
Connection Matrix ◽  
Neural Network Models ◽  
Symmetric Connection

This paper presents the global convergence theory of the discrete-time uniform pseudo projection anti-monotone network with the quasi–symmetric matrix, which removes the connection matrix constraints. The theory widens the range of applications of the discrete–time uniform pseudo projection anti–monotone network and is valid for many kinds of discrete recurrent neural network models.

scholarly journals PHTNet: Characterization and Deep Mining of Involuntary Pathological Hand Tremor using Recurrent Neural Network Models

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Soroosh Shahtalebi ◽  
Seyed Farokh Atashzar ◽  
Olivia Samotus ◽  
Rajni V. Patel ◽  
Mandar S. Jog ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Hand Tremor ◽  
Deep Mining
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