Short-Term Load Forecasting Using EMD with Feature Selection and TCN-Based Deep Learning Model
نویسندگان
چکیده
Short-term load forecasting (STLF) has a significant role in reliable operation and efficient scheduling of power systems. However, it is still major challenge to accurately predict due social natural factors, such as temperature, humidity, holidays weekends, etc. Therefore, very important for the feature selection extraction input data improve accuracy STLF. In this paper, novel hybrid model based on empirical mode decomposition (EMD), one-dimensional convolutional neural network (1D-CNN), temporal (TCN), self-attention mechanism (SAM), long short-term memory (LSTM) proposed fully decompose mine in-depth features forecasting. Firstly, original sequence was decomposed into number sub-series by EMD, Pearson correlation coefficient method (PCC) applied analyzing between with data. Secondly, achieve relationships series external factors during an hour scale correlations among these points, strategy 1D-CNN TCN comprehensively refine extraction. The SAM introduced further enhance key information. Finally, matrix fed According experimental results employing North American New England Control Area (ISO-NE-CA) dataset, more accurate than 1D-CNN, LSTM, TCN, 1D-CNN–LSTM, TCN–LSTM models. outperforms 21.88%, 51.62%, 36.44%, 42.75%, 16.67% 40.48%, respectively, terms mean absolute percentage error.
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ژورنال
عنوان ژورنال: Energies
سال: 2022
ISSN: ['1996-1073']
DOI: https://doi.org/10.3390/en15197170