Wind speed forecasting is critical for wind energy conversion systems. Adaptive and reliable methods and techniques of wind speed forecasting are urgently needed in view of the stochastic nature of wind resource, which is varying from time to time and from site to site. Multi-step-ahead speed forecasting is built with empirical mode decomposition (EMD) method and RBF neural network, which makes use of non-linear and non-stationary signal characteristics. Time series of original wind speed data is decomposed by EMD method. And RBF neural network is used to predict the decomposition of the various components. Experimental results show that the method effectively improves the accuracy and the reliability of wind speed forecasting.

In the wireless sensor networks with multiple mobile sinks, the movement of sinks or failure of sensor nodes may leads to the breakage of existing routes. In order to repair broken path with lower communication overhead in terms of both energy and delay, we propose an efficient routing recovery protocol with endocrine cooperative particle swarm optimization algorithm to establish and optimize the alternative path. With this method, the alternative path from source nodes to the sink with the optimal QoS parameters can be selected. Simulation results demonstrate that ECPSOA can adapt to rapid topological changes with multiple mobile sinks, while decreasing communication overhead and efficiently reducing the energy consumption.