Temporal Attention Convolutional Neural Networks Based on LSTM-Encoder for Time Series Forecasting

Traditional time series prediction algorithms often struggle to model and forecast high-dimensional, complex data from real-world tasks. Here we present a temporal attention convolutional neural network (TACN) based on an LSTM encoder, which leverages attention mechanisms to process long sequences....

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Bibliographic Details
Published in2023 International Conference on Networks, Communications and Intelligent Computing (NCIC) pp. 51 - 54
Main Authors Zhou, Yantong, Chen, Ziwen, Xie, Anqi
Format Conference Proceeding
LanguageEnglish
Published IEEE 17.11.2023
Subjects
Analytical models
Data models
LSTM-encoder
Planning
Predictive models
TACN
Time series analysis
time series prediction
Wind farms
Wind power generation
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Summary:Traditional time series prediction algorithms often struggle to model and forecast high-dimensional, complex data from real-world tasks. Here we present a temporal attention convolutional neural network (TACN) based on an LSTM encoder, which leverages attention mechanisms to process long sequences. For wind power generation scenarios, we combined the LSTM encoding structure with features engineered from sinusoidal and cosine laws of wind speed variation. Experimental comparisons with basic neural network benchmarks demonstrate the superior predictive performance of our TACN model for wind power forecasts. These results indicate potential for applying the approach to practical wind farm management and planning.
DOI:10.1109/NCIC61838.2023.00014