A multi-step short-term photovoltaic power prediction model based on an improved whale migration algorithm

In view of the significant volatility and randomness of photovoltaic power, traditional forecasting methods are unable to meet the requirements for high prediction accuracy. It is urgent to develop a prediction model with high accuracy and strong stability. Therefore, this study proposes a novel multi-step short-term photovoltaic power prediction model based on Variational Mode Decomposition (VMD), Improved Whale Migration Algorithm (IWMA), and Convolutional Neural Network-Kernel Extreme Learning Machine structure (CNN-KELM). Initially, VMD is employed to decompose the original power sequence to reduce its nonlinearity and complexity. Furthermore, we construct a CNN-KELM hybrid model, and the IWMA algorithm, which integrates chaotic mapping, dynamic inertia weight and dynamic factor adjustment with Lévy flight strategy, is introduced to optimize the model parameters, thereby enhancing the prediction performance. Moreover, for each component, a VMD-CNN-IWMA-KELM forecasting model is established, and the predicted results are reconstructed and superimposed to obtain the final prediction. Finally, the performance of the proposed model is validated using two datasets. The experimental results show that the proposed model in this paper shows significant advantages in accuracy and stability. Its goodness-of-fit values reach 96.71% and 92.33%, respectively, effectively improving the accuracy of photovoltaic power prediction.