Robust Multi-Step Predictor for Electricity Markets with Real-Time Pricing

Real-time electricity pricing mechanisms are emerging as a key component of the smart grid. However, prior work has not fully addressed challenges multi-step prediction (Predicting multiple time steps into future) that is accurate, robust and real-time. This paper proposes novel Artificial Intelligence-based approach, Robust Intelligent Price Prediction in (RIPPR), overcomes these challenges. RIPPR utilizes Variational Mode Decomposition (VMD) to transform spot price data stream sub-series optimized for robustness using particle swarm optimization (PSO) algorithm. These inputted Random Vector Functional Link neural network algorithm real-time prediction. A mirror extension removal VMD, including continuous discrete spaces PSO, further contribution improves effectiveness RIPPR. The superiority proposed demonstrated three empirical studies Australian market.