Publication

Abstract : Efficient and accurate prediction of renewable energy sources (RES) is an interminable challenge in efforts to assure the stable and safe operation of any hybrid energy system due to its intermittent nature. High integration of RES especially wind energy into the existing power sector in recent years has made the situation still challenging which draws the attention of many researchers in developing a computationally efficient forecast model for accurately predicting RES. With the advent of Neural network based methods, ELM -Extreme Learning Machine, a typical Single Layer Feedforward Network (SLFFN), has gained a significant attention in recent years in solving various real-time complex problems due to simplified architecture, good generalization capabilities and fast computation. However, since the model parameters are randomly assigned, the conventional ELM is frequently ranked as the second-best model. As a solution, the article attempts to construct a unique optimized Extreme Learning Machine (ELM) based forecast model with improved accuracy for wind speed forecasting. A novel swarm intelligence technique- Lévy flight Chaotic Whale Optimization algorithm (LCWOA) is utilized in the hybrid model to optimize different parameters of ELM. Despite having a appropriate convergence rate, WOA is occasionally unable to discover the global optima due to imbalanced exploration and exploitation when using control parameters with linear variation. An improvement in the convergence rate of WOA can be expected by incorporating chaotic maps in the control parameters of WOA due to their ergodic nature. In addition to this, Lévy flight can significantly improve the intensification and diversification of the Whale Optimization algorithm (WOA) resulting in improvised search ability avoiding local minima. The prediction capability of the suggested hybrid Extreme Learning Machine (ELM) based forecast model is validated with nine other existing models. The experimental study affirms that the suggested model outperform existing forecasting methods in a variety of quantitative metrics.