What question did this study set out to answer?

The aim is to enhance short-term load forecasting accuracy and stability through an integrated LSTM model.

February 21, 2026

Short-term station load forecasting method based on LSTM neural network

Key Points

The aim is to enhance short-term load forecasting accuracy and stability through an integrated LSTM model.
Developed an integrated forecasting model using LSTM, VMD, and ResNet.
Extracted deep power load features based on historical data and influencing factors.
Compared the performance of the proposed model against existing mainstream forecasting models.
The proposed model showed significant improvements in forecasting accuracy.
Demonstrated better stability compared to traditional forecasting methods.

Abstract

With the rapid growth of electricity demand in society, short-term load forecasting is challenging to meet the market demand. To improve the accuracy and stability of system load forecasting, this paper proposes an integrated forecasting model based on LSTM with variational modal decomposition and residual network. The model combines BiLSTM, VMD, and ResNet, extracts power load feature in-depth, and builds an efficient load forecasting system by considering the historical load and various influencing factors. This method fully uses the advantages of neural networks in information processing and effectively solves the problem of insufficiency and instability in short-term forecasting. The experimental results show that the model is significantly better than the existing mainstream prediction models in accuracy.

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Short-term station load forecasting method based on LSTM neural network

Key Points

Abstract

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