A wind speed forecasting system for the construction of a smart grid with two-stage data processing based on improved ELM and deep learning strategies

Abstract

The operation and scheduling management of smart grids are important aspects, and wind speed forecasting modules are indispensable in wind power system management. Researchers have contributed significantly to the development of accurate forecasting models. However, predicting the ideal performance remains a daunting task. Data preprocessing strategies are widely used to process the original wind speed sequences. To develop the utility of the data preprocessing module, a novel forecasting framework based on a two-stage data processing method is designed in this study. The designed system combines singular spectrum analysis and variational mode decomposition methods to decompose the trend term and multiple components of the residual term of the sequence to effectively capture its inherent characteristics. In addition, a multi-objective optimization strategy was applied to determine the weights of the prediction sequences obtained using deep learning techniques and improved extreme learning machine algorithms to obtain accurate forecasting results. The experimental results verified that the proposed wind forecasting framework is better than other benchmark comparison models, thereby establishing a feasible solution for wind speed forecasting and a powerful tool for power grid operation management.