Dynamic Combination Forecasting for Short-Term Photovoltaic Power

Accurate short-term photovoltaic (PV) power prediction can be crucial for fault detection of the control system and reducing the fault of the PV output control system. However, PV power is highly volatile, and significant power fluctuations cannot be adapted to by the combined model when predicting, thus affecting the stable operation of the PV output control system. In response to this issue, a dynamic combination short-term PV power prediction model of temporal convolutional network (TCN)-bidirectional gated recurrent unit network (BiGRU) and TCN-bidirectional long-short term memory network (BiLSTM) based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is proposed. CEEMDAN is employed to decompose the original PV power data to reduce the volatility of the original data. Constructing two combined models, TCN-BiGRU and TCN-BiLSTM, and training them separately. Introducing ElasticNet, which utilizes both L₁ and L₂ regularization terms. This approach preserves the sparsity from least absolute shrinkage and selection operator (LASSO) regression regularization while incorporating the smoothness from Ridge regression regularization, effectively avoiding the issue of the combined model getting trapped in a local optimum. In the end, experimental verification is conducted using actual measurement data from a solar power facility in Gansu, China, and another in Xinjiang, China. The simulation results illustrate that the accuracy of PV power prediction can be significantly improved by the proposed forecasting approach. In comparison with the control experiment, the R² of the Gansu dataset increased by 0.32% at least, and the R² of the Xinjiang dataset increased by 0.66% at least.