A VMD and LSTM based hybrid model of load forecasting for power grid security

L Lv, Z Wu, J Zhang, L Zhang, Z Tan… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
As the basis for the static security of the power grid, power load forecasting directly affects
the safety of grid operation, the rationality of grid planning, and the economy of supply …

[HTML][HTML] A hybrid system based on LSTM for short-term power load forecasting

Y Jin, H Guo, J Wang, A Song - Energies, 2020 - mdpi.com
As the basic guarantee for the reliability and economic operations of state grid corporations,
power load prediction plays a vital role in power system management. To achieve the …

A short-term power load forecasting method based on k-means and SVM

X Dong, S Deng, D Wang - Journal of Ambient Intelligence and …, 2022 - Springer
With the continuous development of smart grids, short-term power load forecasting has
become increasingly important in the operation of power markets and demand-side …

A novel hybrid short-term load forecasting method of smart grid using MLR and LSTM neural network

J Li, D Deng, J Zhao, D Cai, W Hu… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
The short-term load forecasting is crucial in the power system operation and control.
However, due to its nonstationary and complicated random features, an accurate forecast of …

[HTML][HTML] A short-term residential load forecasting model based on LSTM recurrent neural network considering weather features

Y Wang, N Zhang, X Chen - Energies, 2021 - mdpi.com
With economic growth, the demand for power systems is increasingly large. Short-term load
forecasting (STLF) becomes an indispensable factor to enhance the application of a smart …

[HTML][HTML] Short-term load forecasting based on deep learning bidirectional lstm neural network

C Cai, Y Tao, T Zhu, Z Deng - Applied Sciences, 2021 - mdpi.com
Accurate load forecasting guarantees the stable and economic operation of power systems.
With the increasing integration of distributed generations and electrical vehicles, the …

An integrated method based on relevance vector machine for short-term load forecasting

J Ding, M Wang, Z Ping, D Fu, VS Vassiliadis - European Journal of …, 2020 - Elsevier
Short-term electricity load forecasting has become increasingly important due to the
privatization and deregulation in the energy market. This study proposes a probabilistic …

Short-term load forecasting based on LSTM networks considering attention mechanism

J Lin, J Ma, J Zhu, Y Cui - International Journal of Electrical Power & Energy …, 2022 - Elsevier
Reliable and accurate zonal electricity load forecasting is essential for power system
operation and planning. Probabilistic load forecasts can present more comprehensive …

Short-term load forecasting and associated weather variables prediction using ResNet-LSTM based deep learning

X Chen, W Chen, V Dinavahi, Y Liu, J Feng - IEEE Access, 2023 - ieeexplore.ieee.org
Short-term load forecasting is mainly utilized in control centers to explore the changing
patterns of consumer loads and predict the load value at a certain time in the future. It is one …

A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with …

F He, J Zhou, Z Feng, G Liu, Y Yang - Applied energy, 2019 - Elsevier
Short-term load forecasting plays an essential role in the safe and stable operation of power
systems and has always been a vital research issue of energy management. In this …