Robust multi‐step predictor for electricity markets with real‐time pricing

Sachin Kahawala, Daswin De Silva*, Seppo Sierla, Damminda Alahakoon, Rashmika Nawaratne, Evgeny Osipov, Andrew Jennings, Valeriy Vyatkin

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
32 Downloads (Pure)


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

Original languageEnglish
Article number4378
Number of pages20
Issue number14
Publication statusPublished - 2 Jul 2021
MoE publication typeA1 Journal article-refereed


  • Demand response
  • Electricity price forecasting
  • Particle swarm optimization
  • Prosumers
  • Real‐time pricing


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