An intelligent framework for short-term multi-step wind speed forecasting based on Functional Networks

This paper presents a novel method for the development of multi-step wind forecasting models based on functional network (FN), a modern intelligent paradigm. The basis of FN development is the integration of functional theory with neural networks to produce problem-driven network topologies and optimal neural functions with diversified structures as opposed to conventional neural networks. These advantages of functional networks result in optimum models for accurate wind speed and power forecasting. In this research work, FN forecasting engine is developed using three state-of-the-art multi-step forecasting mechanisms, namely, recursive, direct and hybrid DirRec scheme. A detailed analysis of the developed forecast models is carried out using a real-world case study and notable improvement in forecast accuracy is recorded in terms of standard performance indices. Among the three multi-step schemes, hybrid DirRec gives the best forecast accuracy. The results obtained from a comparative analysis against a benchmark model as well as a classical neural network model validate the efficacy of the FN model. Hence the proposed forecasting schemes can be of immense utility for wind power system operators for devising cost-effective energy management and dispatch strategies by accurately forecasting wind power for long forecast horizons.