Social network based smart grids analysis

Renewable energy is an important research issue in recent years, it's also regarded by most of the governments in the world. In order to manage or employ the power well, the aspect of smart gird is proposed to process many kinds of situations renewable energy. Power scheduling is one of the focal points in this research field. By this work, users can understand the volume of power consumption and decide a finer province electricity plan. Based on this concept, renewable energy generation prediction is the approach to enhance the power scheduling and performance of power using. We propose a prediction approach by the theory of social networking and machine learning. We use the SVM, its kernel is RBF, to process the power generation prediction by weather forecasts. The social networking is used to improve the accuracy of the prediction. In the experimental result, the accuracy rate is showed with the excellent results.