Wind Speed Forecasting Using an Ensemble Deep Random Vector Functional Link Neural Network Based on Parsimonious Channel Mixing

Abstract

The electricity generation through wind energy is rapidly expanding, primarily due to its priorities of lower carbon emissions and sustainability. Precise wind speed forecasting is essential for renewable energy conversions as it mitigates the randomness of wind power, therefore aiding in more effective control and strategic planning for power system dispatch. However, the inherent fluctuation of wind speed challenges accurate and consistent time series forecasting. In this paper, we develop a novel parsimonious channel mixing ensemble deep random vector functional link (pcm-edRVFL) network to anticipate future wind speeds. The ensemble deep random vector functional link network (edRVFL) utilizes deep feature extraction and ensemble learning to improve forecasting performance. We refined the standard edRVFL model by incorporating a parsimonious channel mixing selection approach for input data, focusing on crucial historical observations, and strengthening the representation of each explanatory variable. We conduct extensive evaluations on four wind speed datasets using the proposed model, and the comparative experiment results demonstrate its superiority over other baseline models. Our proposed pcm-edRVFL network provides a practical approach for precise and efficient wind speed forecasting, proving to be an instrumental resource in wind energy design and operation systems.