A Two-Layer Nonlinear Combination Method For Short-Term Wind Speed Prediction Based On Elm, Enn, And Lstm

As a typical kind of the Internet of Things, smart grid has attracted a lot of attentions. The power energy management of smart grid is of great importance for energy distribution, system security, and market economics. One of the most important issues is the accurate and stable prediction of wind speed for the optimal operation and management of wind power generations connected to smart grid. In this paper, a novel two-layer nonlinear combination method termed as EEL-ELM is developed for short-term wind speed prediction problems, such as 10-min ahead and 1-h ahead. The first layer is based on extreme learning machine (ELM), Elman neural network (ENN), and long short term memory neural network (LSTM) to separately forecast wind speed by making use of their merits of calculation speed or strong ability in forecasting, and obtain three forecasting results. Then, we propose the second layer by making use of ELM-based nonlinear aggregated mechanism to alleviate the inherent weakness of single method and linear combination. Two real-world case studies, gathered from Inner Mongolia's wind farm in China, are implemented to demonstrate the effectiveness of the proposed EEL-ELM method. By comparing with other eight wind speed prediction methods, the simulation results reveal that EEL-ELM can achieve better forecasting performance according to three evaluation metrics and three statistical tests.