scholarly journals Short-Term Photovoltaic Power Forecasting Based on Long Short Term Memory Neural Network and Attention Mechanism

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 78063-78074 ◽  
Author(s):  
Hangxia Zhou ◽  
Yujin Zhang ◽  
Lingfan Yang ◽  
Qian Liu ◽  
Ke Yan ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Short Term ◽  
Term Memory ◽  
Photovoltaic Power ◽  
Long Short Term Memory ◽  
Power Forecasting

scholarly journals Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagation

2019 ◽  
Vol 15 (10) ◽  
pp. 155014771988313 ◽  
Author(s):  
Chi Hua ◽  
Erxi Zhu ◽  
Liang Kuang ◽  
Dechang Pi
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Photovoltaic Power ◽  
Forecasting Method ◽  
Long Short Term Memory

Accurate prediction of the generation capacity of photovoltaic systems is fundamental to ensuring the stability of the grid and to performing scheduling arrangements correctly. In view of the temporal defect and the local minimum problem of back-propagation neural network, a forecasting method of power generation based on long short-term memory-back-propagation is proposed. On this basis, the traditional prediction data set is improved. According to the three traditional methods listed in this article, we propose a fourth method to improve the traditional photovoltaic power station short-term power generation prediction. Compared with the traditional method, the long short-term memory-back-propagation neural network based on the improved data set has a lower prediction error. At the same time, a horizontal comparison with the multiple linear regression and the support vector machine shows that the long short-term memory-back-propagation method has several advantages. Based on the long short-term memory-back-propagation neural network, the short-term forecasting method proposed in this article for generating capacity of photovoltaic power stations will provide a basis for dispatching plan and optimizing operation of power grid.

scholarly journals An Improved Model for Analyzing Textual Sentiment Based on a Deep Neural Network Using Multi-Head Attention Mechanism

2021 ◽  
Vol 4 (4) ◽  
pp. 85
Author(s):  
Hashem Saleh Sharaf Al-deen ◽  
Zhiwen Zeng ◽  
Raeed Al-sabri ◽  
Arash Hekmat
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Short Term ◽  
Arbitrary Length ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Improved Model

Due to the increasing growth of social media content on websites such as Twitter and Facebook, analyzing textual sentiment has become a challenging task. Therefore, many studies have focused on textual sentiment analysis. Recently, deep learning models, such as convolutional neural networks and long short-term memory, have achieved promising performance in sentiment analysis. These models have proven their ability to cope with the arbitrary length of sequences. However, when they are used in the feature extraction layer, the feature distance is highly dimensional, the text data are sparse, and they assign equal importance to various features. To address these issues, we propose a hybrid model that combines a deep neural network with a multi-head attention mechanism (DNN–MHAT). In the DNN–MHAT model, we first design an improved deep neural network to capture the text's actual context and extract the local features of position invariants by combining recurrent bidirectional long short-term memory units (Bi-LSTM) with a convolutional neural network (CNN). Second, we present a multi-head attention mechanism to capture the words in the text that are significantly related to long space and encoding dependencies, which adds a different focus to the information outputted from the hidden layers of BiLSTM. Finally, a global average pooling is applied for transforming the vector into a high-level sentiment representation to avoid model overfitting, and a sigmoid classifier is applied to carry out the sentiment polarity classification of texts. The DNN–MHAT model is tested on four reviews and two Twitter datasets. The results of the experiments illustrate the effectiveness of the DNN–MHAT model, which achieved excellent performance compared to the state-of-the-art baseline methods based on short tweets and long reviews.

scholarly journals Solar Radiation Prediction Based on Convolution Neural Network and Long Short-Term Memory

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8498
Author(s):  
Tingting Zhu ◽  
Yiren Guo ◽  
Zhenye Li ◽  
Cong Wang
Keyword(s):  
Neural Network ◽  
Solar Irradiance ◽  
Short Term Memory ◽  
Observation Data ◽  
Short Term ◽  
Term Memory ◽  
Photovoltaic Power ◽  
Direct Normal Irradiance ◽  
Long Short Term Memory ◽  
Meteorological Observations

Photovoltaic power generation is highly valued and has developed rapidly throughout the world. However, the fluctuation of solar irradiance affects the stability of the photovoltaic power system and endangers the safety of the power grid. Therefore, ultra-short-term solar irradiance predictions are widely used to provide decision support for power dispatching systems. Although a great deal of research has been done, there is still room for improvement regarding the prediction accuracy of solar irradiance including global horizontal irradiance, direct normal irradiance and diffuse irradiance. This study took the direct normal irradiance (DNI) as prediction target and proposed a Siamese convolutional neural network-long short-term memory (SCNN-LSTM) model to predict the inter-hour DNI by combining the time-dependent spatial features of total sky images and historical meteorological observations. First, the features of total sky images were automatically extracted using a Siamese CNN to describe the cloud information. Next, the image features and meteorological observations were fused and then predicted the DNI in 10-min ahead using an LSTM. To verify the validity of the proposed SCNN-LSTM model, several experiments were carried out using two-year historical observation data provided by the National Renewable Energy Laboratory (NREL). The results show that the proposed method achieved nRMSE of 23.47% and forecast skill of 24.51% for the whole year of 2014, and it also did better than some published methods especially under clear sky and rainy days.

scholarly journals Deep Learning Enhanced Solar Energy Forecasting with AI-Driven IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hangxia Zhou ◽  
Qian Liu ◽  
Ke Yan ◽  
Yang Du
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Energy Generation ◽  
Attention Mechanism ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Short-term photovoltaic (PV) energy generation forecasting models are important, stabilizing the power integration between the PV and the smart grid for artificial intelligence- (AI-) driven internet of things (IoT) modeling of smart cities. With the recent development of AI and IoT technologies, it is possible for deep learning techniques to achieve more accurate energy generation forecasting results for the PV systems. Difficulties exist for the traditional PV energy generation forecasting method considering external feature variables, such as the seasonality. In this study, we propose a hybrid deep learning method that combines the clustering techniques, convolutional neural network (CNN), long short-term memory (LSTM), and attention mechanism with the wireless sensor network to overcome the existing difficulties of the PV energy generation forecasting problem. The overall proposed method is divided into three stages, namely, clustering, training, and forecasting. In the clustering stage, correlation analysis and self-organizing mapping are employed to select the highest relevant factors in historical data. In the training stage, a convolutional neural network, long short-term memory neural network, and attention mechanism are combined to construct a hybrid deep learning model to perform the forecasting task. In the testing stage, the most appropriate training model is selected based on the month of the testing data. The experimental results showed significantly higher prediction accuracy rates for all time intervals compared to existing methods, including traditional artificial neural networks, long short-term memory neural networks, and an algorithm combining long short-term memory neural network and attention mechanism.

Long Short-Term Memory Neural Network-based Power Forecasting of Multi-Core Processors

2021 ◽  
Author(s):  
Mark Sagi ◽  
Martin Rapp ◽  
Heba Khdr ◽  
Yizhe Zhang ◽  
Nael Fasfous ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Power Forecasting
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