Improved Sparrow Search Algorithm-Based Recurrent Neural Network for Short-Term Generation Load Forecasting of Hydropower Stations

To address the challenges of low accuracy and high randomness in short-term hydroelectric load forecasting within Multi-energy Coupled Virtual Power Plants (MC-VPPs), this study proposes a hybrid model integrating Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM) networks, and an Improved Sparrow Search Algorithm (ISSA). Traditional methods, such as exponential smoothing and multiple linear regression, often fail to capture nonlinear dynamics and external disturbances. The proposed framework first decomposes raw load data into four intrinsic mode functions (IMFs) via VMD to extract multi-scale features, including long-term trends, seasonal cycles, and short-term fluctuations. LSTM networks are then applied to model the temporal dependencies of each IMF. To enhance optimization, ISSA introduces a bidirectional sine-cosine search strategy, balancing global exploration and local exploitation to avoid premature convergence. Validated on 1,247 daily load records from a hydropower station in southwestern China, the ISSA-VMD-LSTM model achieves a 30.2% improvement in R², with reductions of 47.2% in RMSE, 47.8% in MAE, and 63.3% in MAPE, outperforming benchmarks like PSO-LSTM and SSA-VMD-LSTM. This demonstrates its robustness in handling nonlinearity and stochasticity. The model enhances MC-VPPs’ operational efficiency by enabling intelligent scheduling and renewable energy integration, with future applications extending to real-time forecasting and other renewable energy systems.

@article{Sun2026,
  title     = {Improved Sparrow Search Algorithm-Based Recurrent Neural Network for Short-Term Generation Load Forecasting of Hydropower Stations},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {1},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Liyuan Sun and Yilun Dong and Junwei Yang},
  doi       = {10.14569/IJACSA.2026.0170122},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170122}
}