Correction: Liu et al. Quarter-Hourly Power Load Forecasting Based on a Hybrid CNN-BiLSTM-Attention Model with CEEMDAN, K-Means, and VMD. Energies 2025, 18, 2675

In the original publication [1], the reference 17, Li, Z.; Wang, Q.; Wang, J.Q.; Qu, H.B.; Dong, J.C.; Dong, Z. ELSTM: An improved long short-term memory network language model for sequence learning. Expert Syst. 2024, 41, e13211, has been retracted. The citation has now been removed in Section 1. Introduction, Paragraph 3 and should read:

“The long short-term memory (LSTM) network, effective in nonlinear tasks like machine translation [17] and speech recognition [18], is increasingly used in load forecasting. It handles temporal and nonlinear data relationships and outperforms traditional methods by capturing long-term dependencies in power data [19,20]. This makes LSTM suitable for addressing the high volatility and uncertainty in power loads, which is crucial as renewable energy penetration increases [21]. However, it struggles to uncover useful information and relationships in non-continuous data [22].”

With this correction, the order of some references has been adjusted accordingly.

In the original publication, there was a mistake in Figure 7 as published. The same picture appeared twice. The unnecessary picture has been removed.

The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.