Power generation efficiency and resources saving of the hydropower industry using the extended data based convolutional neural network

Jiajun Huang, Peihao Zheng, Xuan Hu, Wei Chen, Zhiqiang Geng, Chong Chu and Yongming Han

Renewable Energy, 2025, vol. 243, issue C

Abstract: The electric industry is an important factor affecting social progress and economic growth. Compared to conventional thermal power generation, hydroelectric power is applied as a clean and sustainable form of power generation. The relatively short time of hydropower development and the high difficulty in obtaining hydropower data have contributed to collecting a small sample size of data for building an accurate energy production model. Therefore, a novel convolutional neural network (CNN) integrating the synthetic minority over-sampling technique (SMOTE) algorithm (SMOTE-CNN) is proposed to forecast and enhance the energy setting of hydroelectric power plants with precise yield predictions. The SMOTE algorithm is applied to extend the small sample data to increase the diversity of the sample. Then, the CNN is used to process hydropower data and establish a prediction model. Ultimately, the proposed method is applied to predict the actual data of hydroelectric power plants to achieve energy savings and improve energy utilization efficiency. Compared with the back propagation neural network (BP), the radial basis function neural network (RBF), the extreme learning machine (ELM), the gated recurrent unit (GRU), and the long short-term memory (LSTM), the SMOTE-CNN achieves the best performance in terms of the mean relative error (MRE) and the root mean square error (RMSE), with the MRE is 0.0429 and the RMSE is 2373.7366. Additionally, the optimized allocation of resources can improve power generation efficiency.

Keywords: Production prediction; Production capacity analysis; Energy optimization; Convolutional neural network; Synthetic minority over-sampling technique; Hydropower industry (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:243:y:2025:i:c:s0960148125002034

DOI: 10.1016/j.renene.2025.122541

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