scholarly journals A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3493 ◽  
Author(s):  
Chujie Tian ◽  
Jian Ma ◽  
Chunhong Zhang ◽  
Panpan Zhan
Keyword(s):  
Neural Network ◽  
Real World ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Electrical Load ◽  
Short Term ◽  
Term Memory ◽  
Load Forecast ◽  
Proposed Model ◽  
Long Short Term Memory

Accurate electrical load forecasting is of great significance to help power companies in better scheduling and efficient management. Since high levels of uncertainties exist in the load time series, it is a challenging task to make accurate short-term load forecast (STLF). In recent years, deep learning approaches provide better performance to predict electrical load in real world cases. The convolutional neural network (CNN) can extract the local trend and capture the same pattern, and the long short-term memory (LSTM) is proposed to learn the relationship in time steps. In this paper, a new deep neural network framework that integrates the hidden feature of the CNN model and the LSTM model is proposed to improve the forecasting accuracy. The proposed model was tested in a real-world case, and detailed experiments were conducted to validate its practicality and stability. The forecasting performance of the proposed model was compared with the LSTM model and the CNN model. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were used as the evaluation indexes. The experimental results demonstrate that the proposed model can achieve better and stable performance in STLF.

Production Forecasting with the Interwell Interference by Integrating Graph Convolutional and Long Short-Term Memory Neural Network

2021 ◽  
pp. 1-17
Author(s):  
Enda Du ◽  
Yuetian Liu ◽  
Ziyan Cheng ◽  
Liang Xue ◽  
Jing Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Production Forecasting ◽  
Temporal Correlations ◽  
Proposed Model ◽  
The Mean ◽  
Long Short Term Memory ◽  
The Impact

Summary Accurate production forecasting is an essential task and accompanies the entire process of reservoir development. With the limitation of prediction principles and processes, the traditional approaches are difficult to make rapid predictions. With the development of artificial intelligence, the data-driven model provides an alternative approach for production forecasting. To fully take the impact of interwell interference on production into account, this paper proposes a deep learning-based hybrid model (GCN-LSTM), where graph convolutional network (GCN) is used to capture complicated spatial patterns between each well, and long short-term memory (LSTM) neural network is adopted to extract intricate temporal correlations from historical production data. To implement the proposed model more efficiently, two data preprocessing procedures are performed: Outliers in the data set are removed by using a box plot visualization, and measurement noise is reduced by a wavelet transform. The robustness and applicability of the proposed model are evaluated in two scenarios of different data types with the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE). The results show that the proposed model can effectively capture spatial and temporal correlations to make a rapid and accurate oil production forecast.

scholarly journals Separating overlapping bat calls with a bi-directional long short-term memory network

2019 ◽  
Author(s):  
Kangkang Zhang ◽  
Tong Liu ◽  
Shengjing Song ◽  
Xin Zhao ◽  
Shijun Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Clustering Analysis ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Echolocation Calls ◽  
Significant Difference ◽  
Long Short Term Memory ◽  
Sound Data

AbstractAcquiring clear and usable audio recordings is critical for acoustic analysis of animal vocalizations. Bioacoustics studies commonly face the problem of overlapping signals, but the issue is often ignored, as there is currently no satisfactory solution. This study presents a bi-directional long short-term memory (BLSTM) network to separate overlapping bat calls and reconstruct waveform audio sounds. The separation quality was evaluated using seven temporal-spectrum parameters. The applicability of this method for bat calls was assessed using six different species. In addition, clustering analysis was conducted with separated echolocation calls from each population. Results showed that all syllables in the overlapping calls were separated with high robustness across species. A comparison between the seven temporal-spectrum parameters showed no significant difference and negligible deviation between the extracted and original calls, indicating high separation quality. Clustering analysis of the separated echolocation calls also produced an accuracy of 93.8%, suggesting the reconstructed waveform sounds could be reliably used. These results suggest the proposed technique is a convenient and automated approach for separating overlapping calls using a BLSTM network. This powerful deep neural network approach has the potential to solve complex problems in bioacoustics.Author summaryIn recent years, the development of recording techniques and devices in animal acoustic experiment and population monitoring has led to a sharp increase in the volume of sound data. However, the collected sound would be overlapped because of the existence of multiple individuals, which laid restrictions on taking full advantage of experiment data. Besides, more convenient and automatic methods are needed to cope with the large datasets in animal acoustics. The echolocation calls and communication calls of bats are variable and often overlapped with each other both in the recordings from field and laboratory, which provides an excellent template for research on animal sound separation. Here, we firstly solved the problem of overlapping calls in bats successfully based on deep neural network. We built a network to separate the overlapping calls of six bat species. All the syllables in overlapping calls were separated and we found no significant difference between the separated syllables with non-overlapping syllables. We also demonstrated an instance of applying our method on species classification. Our study provides a useful and efficient model for sound data processing in acoustic research and the proposed method has the potential to be generalized to other animal species.

HUMAN ROBOT INTERACTIVE INTENTION PREDICTION USING DEEP LEARNING TECHNIQUES

2021 ◽  
pp. 1-12
Author(s):  
Thang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
The Neural Network ◽  
Learning Techniques ◽  
Long Short Term Memory ◽  
Deep Learning Neural Network

In this research, we propose a method of human robot interactive intention prediction. The proposed algorithm makes use of a OpenPose library and a Long-short term memory deep learning neural network. The neural network observes the human posture in a time series, then predicts the human interactive intention. We train the deep neural network using dataset generated by us. The experimental results show that, our proposed method is able to predict the human robot interactive intention, providing 92% the accuracy on the testing set.

scholarly journals Attention-Based Long Short-Term Memory Recurrent Neural Network for Capacity Degradation of Lithium-Ion Batteries

Batteries ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 66
Author(s):  
Tadele Mamo ◽  
Fu-Kwun Wang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Capacity Degradation ◽  
Long Short Term Memory

Monitoring cycle life can provide a prediction of the remaining battery life. To improve the prediction accuracy of lithium-ion battery capacity degradation, we propose a hybrid long short-term memory recurrent neural network model with an attention mechanism. The hyper-parameters of the proposed model are also optimized by a differential evolution algorithm. Using public battery datasets, the proposed model is compared to some published models, and it gives better prediction performance in terms of mean absolute percentage error and root mean square error. In addition, the proposed model can achieve higher prediction accuracy of battery end of life.

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 Using Machine Learning Methods in the Financial Market for Technical Analysis Based on Hybrid Models

2020 ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Hybrid Model ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Input Structure ◽  
Long Short Term Memory

In this study, it is presented a new hybrid model based on deep neural networks to predict the direction and magnitude of the Forex market movement in the short term. The overall model presented is based on the scalping strategy and is provided for high frequency transactions. The proposed hybrid model is based on a combination of three models based on deep neural networks. The first model is a deep neural network with a multi-input structure consisting of a combination of Long Short Term Memory layers. The second model is a deep neural network with a multi-input structure made of a combination of one-dimensional Convolutional Neural network layers. The third model has a simpler structure and is a multi-input model of the Multi-Layer Perceptron layers. The overall model was also a model based on the majority vote of three top models. This study showed that models based on Long Short-Term Memory layers provided better results than the other models and even hybrid models with more than 70% accurate.

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