Dynamic Combination Forecasting for Short-Term Photovoltaic Power
Yu Huang, Jiaxing Liu, Zongshi Zhang, Dui Li, Xuxin Li, Guang Wang
Abstract
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 TCN-BiGRU and TCN-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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> regularization terms. This approach preserves the sparsity from 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> of the Gansu data set increased by 0.32% at least, and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> of the Xinjiang data set increased by 0.66% at least.