Renewable energy prediction: A novel short-term prediction model of photovoltaic output power

Photovoltaic power generation is gradually developing into a massive power industry with the maturity of renewable energy power generation technologies. Photovoltaic power generation is greatly affected by external factors and the output power is characterized by randomness and indirectness, which poses a great challenge to photovoltaic grid-connection. A hybrid improved multi-verse optimizer algorithm (HIMVO) is proposed to optimize the support vector machine for photovoltaic output prediction. HIMVO algorithm introduces chaotic sequences to initialize the population, which significantly enhances the convergence rate of the algorithm compared with the multi-universe optimizer algorithm. This study applied particle swarm optimization algorithm, dragonfly algorithm, multi-universe optimizer algorithm and HIMVO to testify the availability of the hybrid improved multi-verse optimizer support vector machine model (HIMVO-SVM). The results indicate that HIMVO algorithm has better optimization ability and stability. The four models, HIMVO-SVM, multi-verse optimizer support vector machine, particle swarm optimization support vector machine, back propagation and radical basis function neural network are used to predict output in three different weather types. The results indicate that the model has higher prediction accuracy and stability. The mean square error value of the HIMVO-SVM model decreases by at least 0.0026, 0.0030 and 0.0012, and the mean absolute percentage error value decreases by at least 3.6768%, 1.9772% and 2.7165%, respectively. The proposed method is beneficial to the prediction of output power and conduces to the economic dispatch of the grid and the maintenance of the stability of the power system.