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Search Results (11)
  • Open Access

    ARTICLE

    Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

    Lin Ma1, Liyong Wang1, Shuang Zeng1, Yutong Zhao1, Chang Liu1, Heng Zhang1, Qiong Wu2,*, Hongbo Ren2

    Energy Engineering, Vol.121, No.6, pp. 1473-1493, 2024, DOI:10.32604/ee.2024.047332

    Abstract Accurate load forecasting forms a crucial foundation for implementing household demand response plans and optimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations, a single prediction model is hard to capture temporal features effectively, resulting in diminished prediction accuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neural network (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), is proposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features from the original data, enhancing the quality of data… More >

  • Open Access

    ARTICLE

    The Influence of Air Pollution Concentrations on Solar Irradiance Forecasting Using CNN-LSTM-mRMR Feature Extraction

    Ramiz Gorkem Birdal*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4015-4028, 2024, DOI:10.32604/cmc.2024.048324

    Abstract Maintaining a steady power supply requires accurate forecasting of solar irradiance, since clean energy resources do not provide steady power. The existing forecasting studies have examined the limited effects of weather conditions on solar radiation such as temperature and precipitation utilizing convolutional neural network (CNN), but no comprehensive study has been conducted on concentrations of air pollutants along with weather conditions. This paper proposes a hybrid approach based on deep learning, expanding the feature set by adding new air pollution concentrations, and ranking these features to select and reduce their size to improve efficiency. In… More >

  • Open Access

    ARTICLE

    Analysis and Modeling of Time Output Characteristics for Distributed Photovoltaic and Energy Storage

    Kaicheng Liu1,3,*, Chen Liang2, Xiaoyang Dong2, Liping Liu1

    Energy Engineering, Vol.121, No.4, pp. 933-949, 2024, DOI:10.32604/ee.2023.043658

    Abstract Due to the unpredictable output characteristics of distributed photovoltaics, their integration into the grid can lead to voltage fluctuations within the regional power grid. Therefore, the development of spatial-temporal coordination and optimization control methods for distributed photovoltaics and energy storage systems is of utmost importance in various scenarios. This paper approaches the issue from the perspective of spatiotemporal forecasting of distributed photovoltaic (PV) generation and proposes a Temporal Convolutional-Long Short-Term Memory prediction model that combines Temporal Convolutional Networks (TCN) and Long Short-Term Memory (LSTM). To begin with, an analysis of the spatiotemporal distribution patterns of More >

  • Open Access

    ARTICLE

    A Time Series Intrusion Detection Method Based on SSAE, TCN and Bi-LSTM

    Zhenxiang He*, Xunxi Wang, Chunwei Li

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 845-871, 2024, DOI:10.32604/cmc.2023.046607

    Abstract In the fast-evolving landscape of digital networks, the incidence of network intrusions has escalated alarmingly. Simultaneously, the crucial role of time series data in intrusion detection remains largely underappreciated, with most systems failing to capture the time-bound nuances of network traffic. This leads to compromised detection accuracy and overlooked temporal patterns. Addressing this gap, we introduce a novel SSAE-TCN-BiLSTM (STL) model that integrates time series analysis, significantly enhancing detection capabilities. Our approach reduces feature dimensionality with a Stacked Sparse Autoencoder (SSAE) and extracts temporally relevant features through a Temporal Convolutional Network (TCN) and Bidirectional Long… More >

  • Open Access

    ARTICLE

    Predicting Reliability and Remaining Useful Life of Rolling Bearings Based on Optimized Neural Networks

    Tiantian Liang*, Runze Wang, Xuxiu Zhang, Yingdong Wang, Jianxiong Yang

    Structural Durability & Health Monitoring, Vol.17, No.5, pp. 433-455, 2023, DOI:10.32604/sdhm.2023.029331

    Abstract In this study, an optimized long short-term memory (LSTM) network is proposed to predict the reliability and remaining useful life (RUL) of rolling bearings based on an improved whale-optimized algorithm (IWOA). The multi-domain features are extracted to construct the feature dataset because the single-domain features are difficult to characterize the performance degeneration of the rolling bearing. To provide covariates for reliability assessment, a kernel principal component analysis is used to reduce the dimensionality of the features. A Weibull distribution proportional hazard model (WPHM) is used for the reliability assessment of rolling bearing, and a beluga… More > Graphic Abstract

    Predicting Reliability and Remaining Useful Life of Rolling Bearings Based on Optimized Neural Networks

  • Open Access

    ARTICLE

    Bi-LSTM-Based Deep Stacked Sequence-to-Sequence Autoencoder for Forecasting Solar Irradiation and Wind Speed

    Neelam Mughees1,2, Mujtaba Hussain Jaffery1, Abdullah Mughees3, Anam Mughees4, Krzysztof Ejsmont5,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6375-6393, 2023, DOI:10.32604/cmc.2023.038564

    Abstract Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050. However, they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions. In microgrids, smart energy management systems, such as integrated demand response programs, are permanently established on a step-ahead basis, which means that accurate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids. With this in mind, a novel “bidirectional long short-term memory network” (Bi-LSTM)-based, deep stacked,… More >

  • Open Access

    ARTICLE

    EliteVec: Feature Fusion for Depression Diagnosis Using Optimized Long Short-Term Memory Network

    S. Kavi Priya*, K. Pon Karthika

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1745-1766, 2023, DOI:10.32604/iasc.2023.032160

    Abstract Globally, depression is perceived as the most recurrent and risky disorder among young people and adults under the age of 60. Depression has a strong influence on the usage of words which can be observed in the form of written texts or stories posted on social media. With the help of Natural Language Processing(NLP) and Machine Learning (ML) techniques, the depressive signs expressed by people can be identified at the earliest stage from their Social Media posts. The proposed work aims to introduce an efficacious depression detection model unifying an exemplary feature extraction scheme and… More >

  • Open Access

    ARTICLE

    Time Series Forecasting Fusion Network Model Based on Prophet and Improved LSTM

    Weifeng Liu1,2, Xin Yu1,*, Qinyang Zhao3, Guang Cheng2, Xiaobing Hou1, Shengqi He4

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3199-3219, 2023, DOI:10.32604/cmc.2023.032595

    Abstract Time series forecasting and analysis are widely used in many fields and application scenarios. Time series historical data reflects the change pattern and trend, which can serve the application and decision in each application scenario to a certain extent. In this paper, we select the time series prediction problem in the atmospheric environment scenario to start the application research. In terms of data support, we obtain the data of nearly 3500 vehicles in some cities in China from Runwoda Research Institute, focusing on the major pollutant emission data of non-road mobile machinery and high emission… More >

  • Open Access

    ARTICLE

    Application of CNN and Long Short-Term Memory Network in Water Quality Predicting

    Wenwu Tan1, Jianjun Zhang1,*, Jiang Wu1, Hao Lan1, Xing Liu1, Ke Xiao2, Li Wang2, Haijun Lin1, Guang Sun3, Peng Guo4

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1943-1958, 2022, DOI:10.32604/iasc.2022.029660

    Abstract Water resources are an indispensable precious resource for human survival and development. Water quality prediction plays a vital role in protecting and enhancing water resources. Changes in water quality are influenced by many factors, both long-term and short-term. Therefore, according to water quality changes’ periodic and nonlinear characteristics, this paper considered dissolved oxygen as the research object and constructed a neural network model combining convolutional neural network (CNN) and long short-term memory network (LSTM) to predict dissolved oxygen index in water quality. Firstly, we preprocessed the water quality data set obtained from the water quality… More >

  • Open Access

    ARTICLE

    Bidirectional Long Short-Term Memory Network for Taxonomic Classification

    Naglaa. F. Soliman1,*, Samia M. Abd Alhalem2, Walid El-Shafai2, Salah Eldin S. E. Abdulrahman3, N. Ismaiel3, El-Sayed M. El-Rabaie2, Abeer D. Algarni1, Fatimah Algarni4, Fathi E. Abd El-Samie1,2

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 103-116, 2022, DOI:10.32604/iasc.2022.017691

    Abstract Identifying and classifying Deoxyribonucleic Acid (DNA) sequences and their functions have been considered as the main challenges in bioinformatics. Advances in machine learning and Deep Learning (DL) techniques are expected to improve DNA sequence classification. Since the DNA sequence classification depends on analyzing textual data, Bidirectional Long Short-Term Memory (BLSTM) algorithms are suitable for tackling this task. Generally, classifiers depend on the patterns to be processed and the pre-processing method. This paper is concerned with a new proposed classification framework based on Frequency Chaos Game Representation (FCGR) followed by Discrete Wavelet Transform (DWT) and BLSTM.… More >

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