November 30, 2025Open Access

Research on Load Forecasting Based on Bayesian Optimized CNN-LSTM Neural Network

Key Points

Load forecasting improves prediction accuracy, addressing limitations in conventional methods.
Bayesian Optimized CNN-LSTM model outperformed traditional LSTM and CNN-LSTM algorithms.
Analysis utilized industrial and agricultural load data from Qinghai Province as training data.
Findings highlight the necessity for advanced forecasting models in evolving power systems.

Abstract

With the high penetration of renewable energy integration and massive user participation in electricity markets, traditional short-term load forecasting methods exhibit limitations in both adaptability and prediction accuracy. There is an urgent need to explore forecasting models that better accommodate the characteristics of new power systems to ensure the accuracy of load forecasting results. To address issues such as high feature dimensions and weak correlations in historical data, and to fully exploit the temporal dependencies in load data, this paper proposes a short-term power load forecasting method based on a Bayesian-optimized Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model. Using typical industrial and agricultural load data from a region in Qinghai Province as training data, experimental results demonstrate that the hybrid forecasting model achieves superior performance compared to standalone LSTM and CNN-LSTM algorithms.

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