scholarly journals Solar Power Prediction using Recurrent Neural Network

2019 ◽  
Vol 7 (5) ◽  
pp. 3327-3331
Author(s):  
Sunil D. Rathod
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Solar Power ◽  
Power Prediction

scholarly journals Application of SRNN-GRU in Photovoltaic power Forecasting

2021 ◽  
Vol 256 ◽  
pp. 02001
Author(s):  
Shiyan Liu ◽  
Xiaoguang Hao ◽  
Zhengji Meng ◽  
Jianfeng Li ◽  
Tongfei Cui ◽  
...  
Keyword(s):  
Neural Network ◽  
Parallel Computing ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Research Result ◽  
Parallel Structure ◽  
Good Prediction ◽  
Power Prediction ◽  
Photovoltaic Power ◽  
Power Forecasting

Short-term photovoltaic power forecasting is of great significance for maintaining the security and stability of the power grid and coordinating the utilization of resources. As one of the Deep Learning Methods, Recurrent Neural Network (RNN) is widely used in time series prediction but lacks the ability of parallel computing. With good prediction effect, RNN is faced with the problem of long training time. In this paper, Sliced Recurrent Neural Network (SRNN) is applied to PV power prediction to guarantee the ability of parallel computing. The research result shows that compared to other commonly used models, SRNN can greatly speed up the training of Deep Learning Network with over 4 times higher training speed of the application of PV power prediction than that of ordinary RNN structure like LSTM and GRU. The accuracy of SRNN model is also improved by 0.1102 mae, which is significantly ahead of the others, as its parallel structure causes the more efficient parameter update, thus achieving ideal effect in PV prediction.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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