scholarly journals Long Short Term Memory Recurrent Neural Network (LSTM-RNN) Based Workload Forecasting Model For Cloud Datacenters

2018 ◽  
Vol 125 ◽  
pp. 676-682 ◽  
Author(s):  
Jitendra Kumar ◽  
Rimsha Goomer ◽  
Ashutosh Kumar Singh
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Forecasting Model ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1856 ◽  
Author(s):  
Munir Husein ◽  
Il-Yop Chung
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Power Output ◽  
Solar Irradiance ◽  
Short Term Memory ◽  
Forecasting Model ◽  
Short Term ◽  
Term Memory ◽  
Irradiance Data ◽  
Long Short Term Memory

In microgrids, forecasting solar power output is crucial for optimizing operation and reducing the impact of uncertainty. To forecast solar power output, it is essential to forecast solar irradiance, which typically requires historical solar irradiance data. These data are often unavailable for residential and commercial microgrids that incorporate solar photovoltaic. In this study, we propose an hourly day-ahead solar irradiance forecasting model that does not depend on the historical solar irradiance data; it uses only widely available weather data, namely, dry-bulb temperature, dew-point temperature, and relative humidity. The model was developed using a deep, long short-term memory recurrent neural network (LSTM-RNN). We compare this approach with a feedforward neural network (FFNN), which is a method with a proven record of accomplishment in solar irradiance forecasting. To provide a comprehensive evaluation of this approach, we performed six experiments using measurement data from weather stations in Germany, U.S.A, Switzerland, and South Korea, which all have distinct climate types. Experiment results show that the proposed approach is more accurate than FFNN, and achieves the accuracy of up to 60.31 W/m2 in terms of root-mean-square error (RMSE). Moreover, compared with the persistence model, the proposed model achieves average forecast skill of 50.90% and up to 68.89% in some datasets. In addition, to demonstrate the effect of using a particular forecasting model on the microgrid operation optimization, we simulate a one-year operation of a commercial building microgrid. Results show that the proposed approach is more accurate, and leads to a 2% rise in annual energy savings compared with FFNN.

A new methodology for kick detection during petroleum drilling using long short-term memory recurrent neural network

2020 ◽  
Vol 142 ◽  
pp. 126-137 ◽  
Author(s):  
Augustine Osarogiagbon ◽  
Somadina Muojeke ◽  
Ramachandran Venkatesan ◽  
Faisal Khan ◽  
Paul Gillard
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Petroleum Drilling
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