A Photovoltaic Power Prediction Approach Based on Data Decomposition and Stacked Deep Learning Model

Liu, Lisang; Guo, Kaiqi; Chen, Jian; Lin, Guiping; Ke, Chengyang; Liang, Jingrun; He, Dongwei

doi:10.3390/electronics12132764

Cited by 6 publications

(2 citation statements)

References 37 publications

(46 reference statements)

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“…CNN is constructed by mimicking biological visual perception mechanisms and is capable of both supervised and unsupervised learning [34]. The sharing of convolutional kernel parameters in the implicit layers and the sparsity of inter-layer connections enable CNN to extract deep local features from high-dimensional data with less computational effort and to obtain effective representations through the convolutional and pooling layers.…”

Section: A Convolutional Layermentioning

confidence: 99%

RSMD-RF-BGSkip Based PV Generation Prediction Method

Xie,

Lin

2024

IEEE Access

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With the increasing use of photovoltaic (PV) power generation, power forecasting has become equally important to maintain stable and economic operation of the power system. However, the high frequency component of the PV power time series reduces the accuracy of the model predictions. Therefore, this paper proposes a short-term prediction model for PV power based on Row Secondary Modal Decomposition (RSMD), Random Forest (RF), and BGSkip neural network. Firstly, modal features with different complexity are obtained by RSMD. Secondly, RF is used to select different modal features. Then, to improve the prediction performance of the model, the BGSkip model employs a hybrid neural network to accurately predict the nonlinear part, while the linear part is handled by an autoregressive model. The prediction results of these two parts are integrated through the BGSkip model to output more accurate prediction values. Finally, the historical data of PV power plants in a region of Liaoning is utilized for experimental validation. The experimental results show that 2 R , MAPE E

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Section: A Convolutional Layermentioning

confidence: 99%

RSMD-RF-BGSkip Based PV Generation Prediction Method

Xie,

Lin

2024

IEEE Access

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“…For the detection and classification of PC, the CNN-BiLSTM model is used. The CNN, a conventional DNN, has fully connected, convolutional, and pooling layers [21].…”

Section: B Detection Modulementioning

confidence: 99%

Sparrow Search Algorithm With Stacked Deep Learning Based Medical Image Analysis for Pancreatic Cancer Detection and Classification

Ramesh,

Abirami,

Gopalakrishnan

et al. 2023

IEEE Access

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Medical image analysis for pancreatic cancer (PC) classification and recognition is a vital domain of research and medical practices. PC is challenging to diagnose and treat; medical imaging approaches aid early diagnosis to analyse and treat, and employ of medical imaging approaches are support early diagnosis, correct analysis, and treatment planning. Computed Tomography (CT) scans are generally utilized to detect and classify PCs. Deep learning (DL) approaches have demonstrated the ability to support the diagnosis and detection of several medical conditions, containing PC. Convolutional Neural Networks (CNNs) are a kind of DL approach generally employed for image analysis that is trained to automatically learn and extract features in medical images. So, this study purposes a new Sparrow Search Algorithm with Stacked Deep Learning based Medical Image Analysis for Pancreatic Cancer Detection and Classification (SSASDL-PCDC) technique on CT images. The purpose of the study is to design an SSASDL-PCDC technique to achieve improved pancreatic cancer detection performance. In addition, the SSASDL-PCDC technique applies Harris Hawks Optimization (HHO) with a densely connected networks (DenseNet) model for the feature extraction process. Moreover, convolutional neural network with bi-directional long shortterm memory (CNN-BiLSTM) approach was utilized for PC detection and classification. Furthermore, Sparrow Search Algorithm (SSA) is used to adjust the hyperparameter values of the CNN-BiLSTM technique. To evaluate the effectiveness of the SSASDL-PCDC technique, extensive experiments were executed on a comprehensive database of pancreatic CT images. The simulation outcome value depicted that the SSASDL-PCDC technique with maximum sensitivity of 99.26%, specificity of 99.26%, and accuracy of 99.26%.INDEX TERMS Pancreatic cancer, computed tomography images, sparrow search algorithm, medical image analysis, cancer diagnosis.The associate editor coordinating the review of this manuscript and approving it for publication was Eduardo Rosa-Molinar .

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Forecasting solar power generation using evolutionary mating algorithm-deep neural networks

Sulaiman,

Mustaffa

2024

Energy and AI

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A Photovoltaic Power Prediction Approach Based on Data Decomposition and Stacked Deep Learning Model

Cited by 6 publications

References 37 publications

RSMD-RF-BGSkip Based PV Generation Prediction Method

RSMD-RF-BGSkip Based PV Generation Prediction Method

Sparrow Search Algorithm With Stacked Deep Learning Based Medical Image Analysis for Pancreatic Cancer Detection and Classification

Forecasting solar power generation using evolutionary mating algorithm-deep neural networks

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