Simultaneous prediction for multiple source–loads based sliding time window and convolutional neural network

Simultaneous prediction for multiple source–loads based sliding time window and convolutional neural network

Practical applications of perception, communication, computing, etc. in modern energy industry continually generate large scale data from diversified monitoring terminals. Such massive information has the characteristics like heterogeneity, time sequence, low value density, etc. To promptly obtain h...

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Bibliographic Details
Main Authors: Li, Q. (Author), Ouyang, H. (Author), Yang, T. (Author), Zhang, G. (Author), Zhang, L. (Author), Zhen, L. (Author)
Format: Article
Language:English
Published: Elsevier Ltd 2022
Subjects:
Convolution
Convolutional neural network
Convolutional neural networks
Energy industry
Energy source
Extraction
Forecasting
Information filtering
Large scale data
Median filters
Multiple source
Multiple source–load
Multiple source–loads
Parallel strategies
Prediction
Sampling
Sliding time window
Sliding time windows
Time sequences
Time series analysis
Training sample
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