Accurate prediction of the traffic state can help to address the issue of traffic congestion, providing guiding advices for people's travel and traffic regulation. In this paper, we propose a novel short-term traffic flow prediction approach based on empirical mode decomposition and combination model fusion. First, we explore the amplitude-frequency characteristics of short-term traffic flow series, and use empirical mode decomposition to decompose traffic flow to several components with different frequency. Second, based on the results of self-similarity analysis of each component, improved extreme learning machine, seasonal auto regressive integrated moving average and auto regressive moving average are selected to predict different components. Meanwhile, an improved fruit fly optimization algorithm is proposed to optimize the weight coefficient of the combination model. Third, the prediction results of each prediction model are multiplied by their respective weight coefficient to get the final prediction results. We evaluate our prediction approach by doing thorough experiment on a real traffic data set. Moreover, experimental results show that the proposed approach has superior performance than state-of-the-art prediction methods or models in short-term traffic flow prediction.

Short-term wind speed prediction is of importance for power grids. It can mitigate the disadvantageous impacts of wind farms on power systems and enhance the competitiveness of wind power in electricity markets. A short-term wind speed prediction model is proposed. Many useless neurons of incremental extreme learning machine have little influences on the final output, at the same time, reduce the efficiency of the algorithm. In order to solve this problem, based on error minimized extreme learning machine, an improved particle swarm optimization algorithm is proposed to decrease the number of useless neurons, achieve the goal of reducing the network complexity and improving the efficiency of the algorithm. The stability and convergence of the algorithm are proved. The actual short-term wind speed time series is used as the research object. Multistep prediction simulation of short-term wind speed is performed out. Compared with the other prediction models, the simulation results show that the prediction model proposed in this paper reduces the training time of the model and decreases the number of hidden layer nodes. The prediction model has higher prediction accuracy and reliability, meanwhile improve the prediction performance indicators.

The prediction accuracy of wind power affects the operation cost of the power grid, which is a direct result of the supply and demand balance of the grid. Therefore, how to improve the prediction accuracy of wind power is very important. Considering the prediction accuracy of current prediction methods is not high, a wind power prediction method based on a hybrid kernel function support vector machine is proposed. On the basis of the exhibited characteristics of different kernel functions, the hybrid kernel function is a linear combination of the radial basis function and the polynomial kernel function. The hybrid kernel function is selected as the kernel function of support vector machine. The global kernel function is used to fit the correlation of the distant sample data, while the partial kernel function is used to fit the correlation of the data in neighboring fields. The generalization performance of the support vector machine model is improved. At the same time, an improved particle swarm optimization algorithm is introduced to determine the optimal parameters of the hybrid kernel function and support vector machine prediction model. Finally, the built prediction model is used to predict the wind power. The simulation results demonstrate that the proposed prediction method has better prediction accuracy for wind power.