Abstract
The accuracy of short-term load forecasting plays an important role in the operation of the power system. However, because of the randomness of load data, it is a difficult task to provide accurate load forecasting. In this work, a short-term load forecasting approach integrating empirical mode decomposition (EMD), bidirectional long short-term memory (BiLSTM) and attention mechanism is proposed. At first, the electric load series are decomposed into several intrinsic mode functions (IMFs) by EMD. Then a BiLSTM neural network based on attention mechanism is applied on each of the extracted IMFs to predict the tendencies of these IMFs. At last, the prediction results of all IMFs are combined to get the final prediction result of electric load. The proposed approach is evaluated on a real-world dataset from Australian Energy Market Operator. The experimental results demonstrates that the prediction accuracy of the proposed approach can be greatly improved, compared with the other 7 benchmark models. The experiments also showed that the recommended number of IMFs are either 3 or 4 based on both prediction accuracy and running time.
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Australian Energy Market Operator (AEMO). Available online: www.aemo.com.au. Accessed Aug 2021
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Meng, Z., Xie, Y. & Sun, J. Short-term load forecasting using neural attention model based on EMD. Electr Eng 104, 1857–1866 (2022). https://doi.org/10.1007/s00202-021-01420-4
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DOI: https://doi.org/10.1007/s00202-021-01420-4