VMD-AC-LSTM: An Accurate Prediction Method for Solar Irradiance

Currently, solar power has become one of the most promising new power generation methods. But electricity cannot be stored directly and solar power has strong volatility, therefore the short-term accurate prediction of solar irradiance is of great significance to maintain the stable operation of the power grid. This work presents a novel decomposition integrated deep learning model, VMD-AC-BiLSTM, is proposed for ultra-short-term prediction of solar irradiance. The proposed model organically combines Variational Modal Decomposition (VMD), Multi-head Self-Attention Mechanism, One-Dimensional Convolutional Neural Network (1D-CNN) and Bidirectional Long and Short-Term Memory Network (BiLSTM). Firstly, the historical data are decomposed into several modal components by VMD, and these components are divided into stochastic and trend component sets according to their frequency ranges. Then the stochastic and periodicity of solar irradiance are predicted by two different prediction modules. The prediction results of the two modules are integrated at the end of the proposed model. Meanwhile, the proposed model also considers the complex effects of cloud type and solar zenith angle with stochasticity and periodicity in solar irradiance data, respectively. The experimental results show that the proposed model produces relatively accurate solar irradiance predictions under different evaluation criteria. And the proposed model has higher prediction accuracy and robustness compared to other deep learning models.