To predict PM2.5 by a deep learning method of long-short term memory network - A case study of Kaohsiung City

Load forecasting is a key issue for efficient real-time energy management in smart grids. To control the load using demand side management accurately, load forecasting should be predicted in the short term. With the advent of advanced measuring infrastructure, it is possible to measure energy consumption at sampling rates up to every 5 min and analyze the load profile of small-scale energy groups, such as individual buildings. This paper presents applications of deep learning using feature decomposition for improving the accuracy of load forecasting. The load profile is decomposed into a weekly load profile and then decomposed into intrinsic mode functions by variational mode decomposition to capture periodic features. Then, a long short-term memory network model is trained by three-dimensional input data with three-step regularization. Finally, the prediction results of all intrinsic mode functions are combined with advanced measuring infrastructure measured in the previous steps to determine an aggregated output for load forecasting. The results are validated by applications to real-world data from smart buildings, and the performance of the proposed approach is assessed by comparing the predicted results with those of conventional methods, nonlinear autoregressive networks with exogenous inputs, and long short-term memory network-based feature decomposition.

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A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory

Sensors ◽

10.3390/s21196614 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6614

Author(s):

Yangde Gao ◽

Cheol Hong Kim ◽

Jong-Myon Kim

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Short Term Memory ◽

Rotating Machinery ◽

Identification Accuracy ◽

Learning Method ◽

Short Term ◽

Deep Convolutional Neural Networks ◽

Term Memory ◽

Long Short Term Memory

Deep learning (DL) plays a very important role in the fault diagnosis of rotating machinery. To enhance the self-learning capacity and improve the intelligent diagnosis accuracy of DL for rotating machinery, a novel hybrid deep learning method (NHDLM) based on Extended Deep Convolutional Neural Networks with Wide First-layer Kernels (EWDCNN) and long short-term memory (LSTM) is proposed for complex environments. First, the EWDCNN method is presented by extending the convolution layer of WDCNN, which can further improve automatic feature extraction. The LSTM then changes the geometric architecture of the EWDCNN to produce a novel hybrid method (NHDLM), which further improves the performance for feature classification. Compared with CNN, WDCNN, and EWDCNN, the proposed NHDLM method has the greatest performance and identification accuracy for the fault diagnosis of rotating machinery.

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Long Short-Term Memory (LSTM) Deep Learning Method for Intrusion Detection in Network Security

International Journal of Engineering Research and ◽

10.17577/ijertv9is061016 ◽

2020 ◽

Vol V9 (06) ◽

Author(s):

Supriya Shende ◽

Keyword(s):

Deep Learning ◽

Network Security ◽

Intrusion Detection ◽

Short Term Memory ◽

Learning Method ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Health Monitoring Framework for Weather Radar Based on Long Short-Term Memory Network with a Real Case Study

Journal of Sensors ◽

10.1155/2022/5386341 ◽

2022 ◽

Vol 2022 ◽

pp. 1-10

Author(s):

Wei Li ◽

Dalin Wang ◽

Wei Zhou ◽

Yimeng Wang ◽

Chao Shen

Keyword(s):

Health Monitoring ◽

Short Term Memory ◽

Weather Radar ◽

Real Case ◽

Short Term ◽

Term Memory ◽

Monitoring Framework ◽

Memory Network ◽

Long Short Term Memory

The health management of weather radar plays a key role in achieving timely and accurate weather forecasting. The current practice mainly exploits a fixed threshold prespecified for some monitoring parameters for fault detection. This causes abundant false alarms due to the evolving working environments, increasing complexity of the modern weather radar, and the ignorance of the dependencies among monitoring parameters. To address the above issues, we propose a deep learning-based health monitoring framework for weather radar. First, we develop a two-stage approach for problem formulation that address issues of fault scarcity and abundant false fault alarms in processing the databases of monitoring data, fault alarm record, and maintenance records. The temporal evolution of weather radar under healthy conditions is represented by a long short-term memory network (LSTM) model. As such, any anomaly can be identified according to the deviation between the LSTM-based prediction and the actual measurement. Then, construct a health indicator based on the portion of the occurrence of deviation beyond a user-specified threshold within a time window. The proposed framework is demonstrated by a real case study for the Chinese S-band weather radar (CINRAD-SA). The results validate the effectiveness of the proposed framework in providing early fault warnings.

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