Aug 2026· International Journal of Data Science and Analysis· Vol 22· 0 citations· 34 references
TL;DR
The results of the experiment indicate that the proposed model enhances the accuracy and efficiency of power load forecasting, offering more reliable support for power planning and dispatching.
Accurate wind power forecasting is essential for secure and economical power system operation, yet the strong non-stationarity and multiscale fluctuations of wind power signals pose significant challenges to conventional models.
This study proposes M2DAF, a hybrid framework that integrates an adaptive multil...
Hai-Tao Xiong, Ping Zhao, Yuan-Yuan Cai· Data Technologies and Applic...· 0 citations
Comparing the efficacy of the various deep learning techniques for short term load prediction shows that Convolutional Neural Network based BiLSTM technique is a better option for short term load forecasting.
J. Thomas, Krishna Veni Kaliappan Sivakumar· ITEGAM- Journal of Engineeri...· 0 citations
Power load forecasting is a key task in power system operation and control. Due to the non-stationarity and complex randomness of power loads, it is challenging to accurately predict power loads. A short-term load forecasting method is proposed based on a hybrid decomposition approach and time convolutional network wit...
Linlin Tong, Zhao Zhang, Jialong Hou et al.· Transactions of the Institut...· 0 citations
A novel forecast framework integrating improved complete ensemble empirical mode decomposition with adaptive noise, sample entropy reconstruction, a Transformer bidirectional long- short term memory (Transformer-BiLSTM) hybrid neural network, and the RIME optimization algorithm is proposed.
Yi-Ming Jin, Hao-Yu Wu, Jian-Fei Chen· Journal of Energy Engineerin...· 0 citations
With the rapid growth of wind power penetration, the inherent randomness and uncertainty of wind power pose serious challenges to the stable operation of power systems. To address this issue, this paper proposes a wind power forecasting model based on dual decomposition. The model first applies Variational Mode Decompo...
Rui Huang, Jia-Yi Li, Ying-Ying Wang et al.· European Conference on Elect...· 0 citations
Accurate power consumption forecasting is important for energy management, smart grid operation, and efficient electricity utilization. Deep learning models can capture nonlinear relationships and temporal patterns in power consumption data, but their performance may be affected by overfitting. This study presents a co...
Pakrigna Long, M. K. Abdul Kadir, K. Vijyakumar· 2026 IEEE International Conf...· 0 citations
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