Designing a long short-term network for short-term forecasting of global horizontal irradiance

AbstractLong short-term memory (LSTM) models based on specialized deep neural network-based architecture have emerged as an important model for forecasting time-series. However, the literature does not provide clear guidelines for design choices, which affect forecasting performance. Such choices include the need for pre-processing techniques such as deseasonalization, ordering of the input data, network size, batch size, and forecasting horizon. We detail this in the context of short-term forecasting of global horizontal irradiance, an accepted proxy for solar energy. Particularly, short-term forecasting is critical because the cloud conditions change at a sub-hourly having large impacts on incident solar radiation. We conduct an empirical investigation based on data from three solar stations from two climatic zones of India over two seasons. From an application perspective, it may be noted that despite the thrust given to solar energy generation in India, the literature contains few instances of robust studies across climatic zones and seasons. The model thus obtained subsequently outperformed three recent benchmark methods based on random forest, recurrent neural network, and LSTM, respectively, in terms of forecasting accuracy. Our findings underscore the importance of considering the temporal order of the data, lack of any discernible benefit from data pre-processing, the effect of making the LSTM model stateful. It is also found that the number of nodes in an LSTM network, as well as batch size, is influenced by the variability of the input data.

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Extraction of local and global features by a convolutional neural network–long short-term memory network for diagnosing bearing faults

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/09544062211016505 ◽

2021 ◽

pp. 095440622110165

Author(s):

Zhang Chao ◽

Wang Wei-zhi ◽

Zhang Chen ◽

Fan Bin ◽

Wang Jian-guo ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Condition Monitoring ◽

Short Term Memory ◽

Vibration Signal ◽

Short Term ◽

Global Features ◽

Term Memory ◽

Long Short Term Memory ◽

Lstm Network

Accurate and reliable fault diagnosis is one of the key and difficult issues in mechanical condition monitoring. In recent years, Convolutional Neural Network (CNN) has been widely used in mechanical condition monitoring, which is also a great breakthrough in the field of bearing fault diagnosis. However, CNN can only extract local features of signals. The model accuracy and generalization of the original vibration signals are very low in the process of vibration signal processing only by CNN. Based on the above problems, this paper improves the traditional convolution layer of CNN, and builds the learning module (local feature learning block, LFLB) of the local characteristics. At the same time, the Long Short-Term Memory (LSTM) is introduced into the network, which is used to extract the global features. This paper proposes the new neural network—improved CNN-LSTM network. The extracted deep feature is used for fault classification. The improved CNN-LSTM network is applied to the processing of the vibration signal of the faulty bearing collected by the bearing failure laboratory of Inner Mongolia University of science and technology. The results show that the accuracy of the improved CNN-LSTM network on the same batch test set is 98.75%, which is about 24% higher than that of the traditional CNN. The proposed network is applied to the bearing data collection of Western Reserve University under the condition that the network parameters remain unchanged. The experiment shows that the improved CNN-LSTM network has better generalization than the traditional CNN.

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Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting

Water ◽

10.3390/w11071387 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1387 ◽

Cited By ~ 50

Author(s):

Le ◽

Ho ◽

Lee ◽

Jung

Keyword(s):

Neural Network ◽

River Basin ◽

Input Data ◽

Short Term Memory ◽

Flood Forecasting ◽

Data Sets ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Hoa Binh

Flood forecasting is an essential requirement in integrated water resource management. This paper suggests a Long Short-Term Memory (LSTM) neural network model for flood forecasting, where the daily discharge and rainfall were used as input data. Moreover, characteristics of the data sets which may influence the model performance were also of interest. As a result, the Da River basin in Vietnam was chosen and two different combinations of input data sets from before 1985 (when the Hoa Binh dam was built) were used for one-day, two-day, and three-day flowrate forecasting ahead at Hoa Binh Station. The predictive ability of the model is quite impressive: The Nash–Sutcliffe efficiency (NSE) reached 99%, 95%, and 87% corresponding to three forecasting cases, respectively. The findings of this study suggest a viable option for flood forecasting on the Da River in Vietnam, where the river basin stretches between many countries and downstream flows (Vietnam) may fluctuate suddenly due to flood discharge from upstream hydroelectric reservoirs.

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Underground Cylindrical Objects Detection and Diameter Identification in GPR B-Scans via the CNN-LSTM Framework

Electronics ◽

10.3390/electronics9111804 ◽

2020 ◽

Vol 9 (11) ◽

pp. 1804

Author(s):

Wentai Lei ◽

Jiabin Luo ◽

Feifei Hou ◽

Long Xu ◽

Ruiqing Wang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Short Term Memory ◽

Vital Role ◽

Short Term ◽

Term Memory ◽

Region Detection ◽

Field Environment ◽

Long Short Term Memory ◽

Lstm Network

Ground penetrating radar (GPR), as a non-invasive instrument, has been widely used in the civil field. The interpretation of GPR data plays a vital role in underground infrastructures to transfer raw data to the interested information, such as diameter. However, the diameter identification of objects in GPR B-scans is a tedious and labor-intensive task, which limits the further application in the field environment. The paper proposes a deep learning-based scheme to solve the issue. First, an adaptive target region detection (ATRD) algorithm is proposed to extract the regions from B-scans that contain hyperbolic signatures. Then, a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) framework is developed that integrates Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network to extract hyperbola region features. It transfers the task of diameter identification into a task of hyperbola region classification. Experimental results conducted on both simulated and field datasets demonstrate that the proposed scheme has a promising performance for diameter identification. The CNN-LSTM framework achieves an accuracy of 99.5% on simulated datasets and 92.5% on field datasets.

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Two-Step Biometrics Using Electromyogram Signal Based on Convolutional Neural Network-Long Short-Term Memory Networks

Applied Sciences ◽

10.3390/app11156824 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6824

Author(s):

Jin-Su Kim ◽

Min-Gu Kim ◽

Sung-Bum Pan

Keyword(s):

Neural Network ◽

Data Fusion ◽

Convolutional Neural Network ◽

Gesture Recognition ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Emg Signal ◽

Long Short Term Memory ◽

Lstm Network

Electromyogram (EMG) signals cannot be forged and have the advantage of being able to change the registered data as they are characterized by the waveform, which varies depending on the gesture. In this paper, a two-step biometrics method was proposed using EMG signals based on a convolutional neural network–long short-term memory (CNN-LSTM) network. After preprocessing of the EMG signals, the time domain features and LSTM network were used to examine whether the gesture matched, and single biometrics was performed if the gesture matched. In single biometrics, EMG signals were converted into a two-dimensional spectrogram, and training and classification were performed through the CNN-LSTM network. Data fusion of the gesture recognition and single biometrics was performed in the form of an AND. The experiment used Ninapro EMG signal data as the proposed two-step biometrics method, and the results showed 83.91% gesture recognition performance and 99.17% single biometrics performance. In addition, the false acceptance rate (FAR) was observed to have been reduced by 64.7% through data fusion.

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Audio Event Identification and Classification for Cricket Sports using LSTM

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d9462.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 9924-9927

Keyword(s):

Neural Network ◽

Short Term Memory ◽

Research Work ◽

Short Term ◽

Mel Frequency Cepstral Coefficients ◽

Event Classification ◽

Event Identification ◽

Audio Event ◽

Long Short Term Memory ◽

Lstm Network

Audio event identification is an emerging research topic to augment the automation of audio tagging, context-based audio event retrieval, audio surveillance and much more. In this research work, audio event classification for cricket commentary is done by using long short term memory (LSTM) neural network. Mel-frequency cepstral coefficients (MFCC) features are extracted from the audio commentary and trained with LSTM neural network. The trained LSTM network is validated and attained an accuracy of 95%.

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Investigation on recognition method of acoustic emission signal of the compressor valve based on CNN and LSTM network

E3S Web of Conferences ◽

10.1051/e3sconf/202125202023 ◽

2021 ◽

Vol 252 ◽

pp. 02023

Author(s):

Yanfeng Wang ◽

Jin Wang ◽

Junwei Sun ◽

Enhao Liang ◽

Tao Wang

Keyword(s):

Neural Network ◽

Acoustic Emission ◽

Dynamic Characteristics ◽

Short Term Memory ◽

Reciprocating Compressor ◽

Short Term ◽

Emission Signal ◽

Ae Signal ◽

Long Short Term Memory ◽

Lstm Network

The valve is one of the important parts of the reciprocating compressor, which directly affects the thermodynamic process and reliability of the compressor. In this paper, acoustic emission (AE) technology is used to predict the dynamic characteristics of valves. The AE signal of the compressor valve is analyzed based on the deep learning method, and the mapping relation between the AE signal and the dynamic characteristics of the valve is obtained. The results show that the prediction accuracy of the models trained by Long Short-Term Memory (LSTM) artificial neural network and Convolutional Neural Network (CNN) is 97% and 95%, respectively, which can accurately predict the dynamic characteristics of the valve. Although the prediction results of CNN are slightly lower than that of LSTM network, the calculation speed of CNN is relatively faster.

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Time Series Multiple Channel Convolutional Neural Network with Attention-Based Long Short-Term Memory for Predicting Bearing Remaining Useful Life

Sensors ◽

10.3390/s20010166 ◽

2019 ◽

Vol 20 (1) ◽

pp. 166 ◽

Cited By ~ 4

Author(s):

Jehn-Ruey Jiang ◽

Juei-En Lee ◽

Yi-Ming Zeng

Keyword(s):

Neural Network ◽

Time Series ◽

Convolutional Neural Network ◽

Short Term Memory ◽

Remaining Useful Life ◽

Multiple Channels ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Lstm Network

This paper proposes two deep learning methods for remaining useful life (RUL) prediction of bearings. The methods have the advantageous end-to-end property that they take raw data as input and generate the predicted RUL directly. They are TSMC-CNN, which stands for the time series multiple channel convolutional neural network, and TSMC-CNN-ALSTM, which stands for the TSMC-CNN integrated with the attention-based long short-term memory (ALSTM) network. The proposed methods divide a time series into multiple channels and take advantage of the convolutional neural network (CNN), the long short-term memory (LSTM) network, and the attention-based mechanism for boosting performance. The CNN performs well for extracting features from data with multiple channels; dividing a time series into multiple channels helps the CNN extract relationship among far-apart data points. The LSTM network is excellent for processing temporal data; the attention-based mechanism allows the LSTM network to focus on different features at different time steps for better prediction accuracy. PRONOSTIA bearing operation datasets are applied to the proposed methods for the purpose of performance evaluation and comparison. The comparison results show that the proposed methods outperform the others in terms of the mean absolute error (MAE) and the root mean squared error (RMSE) of RUL prediction.

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