Renewable energy power prediction plays a crucial role in the development of renewable energy generation, and it also faces a challenging issue because of the uncertainty and complex fluctuation caused by environmental and climatic factors. In recent years, deep learning has been increasingly applied in the time series prediction of new energy, where Deep Belief Networks (DBN) can perform outstandingly for learning of nonlinear features. In this paper, we employed the DBN as the prediction model to forecast wind power and PV power. A novel metaheuristic optimization algorithm, called swarm spider optimization (SSO), was utilized to optimize the parameters of the DBN so as to improve its performance. The SSO is a novel swarm spider behavior based optimization algorithm, and it can be employed for addressing complex optimization and engineering problems. Considering that the prediction performance of the DBN is affected by the number of the nodes in the hidden layer, the SSO is used to optimize this parameter during the training stage of DBN (called SSO-DBN), which can significantly enhance the DBN prediction performance. Two datasets, including wind power and PV power with their influencing factors, were used to evaluate the forecasting performance of the proposed SSO-DBN. We also compared the proposed model with several well-known methods, and the experiment results demonstrate that the proposed prediction model has better stability and higher prediction accuracy in comparison to other methods.

Accurate photovoltaic (PV) power forecasting is indispensable to enhancing the stability of the power grid and expanding the absorptive photoelectric capacity of the power grid. As an excellent nonlinear regression model, the relevance vector machine (RVM) can be employed to forecast PV power. However, the optimization of the free parameters is still a key problem for improving the performance of the RVM. Taking advantage of the strong global search capability, good stability, and fast convergence rate of the sparrow search algorithm (SSA), this paper optimizes the parameters of the RVM by using the SSA to develop an excellent RVM (called SSA-RVM). Consequently, a novel hybrid PV power forecasting model via the SSA-RVM is proposed to perform ultra-short-term (4 h ahead) prediction. In addition, the effects of seasonal distribution and weather type on PV power are fully considered, and different seasonal prediction models are established separately to improve the prediction capability. The benchmark is used to verify the accuracy of the SSA-RVM-based forecasting model under various conditions, and the experiment results demonstrate that the proposed SSA-RMV method outperforms the traditional RVM and support vector machine models, and it even shows a better prediction effect than the RVM models with other optimization approaches.