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• W4318615977 abstract "Electricity time series prediction is a fundamental part in electricity system scheduling that maintains the balance between electrical supply and demand. However, most existing methods cannot capture the complicated structure of electricity time series, and make personalized suggestions on electricity purchasing scheme. The main challenge lies in the periodicity and instability of electricity time series. To capture the global and local features simultaneously, we propose a Huffman Tree based Multi-resolution Temporal Convolution Network (HM-TCN). HM-TCN can extract and integrate multi-resolution features and therefore achieve high prediction accuracy. We also implement a cost optimization framework based on HM-TCN with visualization interface. Extensive experiments with real-world data offer evidence that the proposed method outperform the baselines by reducing RMSE by at least 89%." @default.
• W4318615977 created "2023-01-31" @default.
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• W4318615977 date "2023-01-01" @default.
• W4318615977 modified "2023-09-26" @default.
• W4318615977 title "Huffman Tree Based Multi-resolution Temporal Convolution Network for Electricity Time Series Prediction" @default.
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• W4318615977 doi "https://doi.org/10.1007/978-3-031-26118-3_18" @default.
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