Hour-ahead photovoltaic generation forecasting method based on machine learning and multi objective optimization algorithm

• In the light of the decomposition and ensemble mechanism, the designed system decomposes the original PV power series, reduces the high-frequency noise, so as to reconstructs the sequences. • A novel combination of denoising parameter intelligent optimization, and weights determined strategy. • On the basis of four ANNs, the features of the PV power sequences can be better gained and used. • To further explore the efficiency of the designed system, we have theoretically proved that the hybrid predictive system can obtained the pareto optimal solution. As the penetration rate of solar energy in the grid continues to enhance, solar power photovoltaic generation forecasts have become an indispensable aspect of mechanism mobilization and maintenance of the stability of the power system. In this regard, many researchers have done a lot of study, and put forward some predictive models. However, many individual prediction systems only consider the prediction accuracy rate without further considering the prediction utility and stability. To fill this gap, a comprehensive system is designed in this paper, which is on the basis of automatic optimization of variational mode decomposition mechanism, and the weight of system is determined by multi objective intelligent optimization algorithm. In particular, it can be proved theoretically that the developed predictive system can achieve the pareto optimal solution. And the designed system is shown to be very effective in forecasting the 2021 photovoltaic power data obtained from Belgium. The empirical study reports that the combination of variational mode decomposition strategy based on genetic algorithm and multi objective grasshopper optimization algorithm is found to be the satisfactory strategy to optimize the predictive system compared with other common mechanism. And the results of several numerical studies show that the designed predictive system achieves the superior performance as compared to the control systems, and in multi-step forecasting, the designed system has better stability than the comparison systems.