Year: 2025
Type: article
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.
Source: Energies
DOI: 10.3390/en18236217
Authors: Pengyang Duan, Huannian Jiao, Jianying Sun, Aiming Han, Zheng Dai, Liang Cheng, Xiaotao Chen
Institutions: State Grid Corporation of China (China), Qinghai University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4416764999