An adaptive hybrid ensemble with pattern similarity analysis and error correction for short-term load forecasting

Laouafi, Abderrezak; Laouafi, Farida; Boukelia, Taqiy Eddine

doi:10.1016/j.apenergy.2022.119525

Cited by 15 publications

(1 citation statement)

References 64 publications

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“…Wei et al [45] proposed detrend singular spectrum fluctuation analysis (DSSFA) to extract trend and periodic components and then input these components into LSTM to improve short-term forecasting accuracy. Laouafi et al [46] proposed an adaptive hybrid ensemble method named CMKP-EG-SVR and optimized the result of the mixture model through a gaussian-based error correction strategy.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Residential Short-Term Load Forecasting Method Using Attention Mechanism and Deep Learning

Huang

Chen

et al. 2022

Buildings

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Development in economics and social society has led to rapid growth in electricity demand. Accurate residential electricity load forecasting is helpful for the transformation of residential energy consumption structure and can also curb global climate warming. This paper proposes a hybrid residential short-term load forecasting framework (DCNN-LSTM-AE-AM) based on deep learning, which combines dilated convolutional neural network (DCNN), long short-term memory network (LSTM), autoencoder (AE), and attention mechanism (AM) to improve the prediction results. First, we design a T-nearest neighbors (TNN) algorithm to preprocess the original data. Further, a DCNN is introduced to extract the long-term feature. Secondly, we combine the LSTM with the AE (LSTM-AE) to learn the sequence features hidden in the extracted features and decode them into output features. Finally, the AM is further introduced to extract and fuse the high-level stage features to achieve the prediction results. Experiments on two real-world datasets show that the proposed method is good at capturing the oscillation characteristics of low-load data and outperforms other methods.

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Section: Related Workmentioning

confidence: 99%