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

Author

Listed:
  • Sachin Kahawala

    (Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3083, Australia)

  • Daswin De Silva

    (Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3083, Australia)

  • Seppo Sierla

    (Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland)

  • Damminda Alahakoon

    (Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3083, Australia)

  • Rashmika Nawaratne

    (Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3083, Australia)

  • Evgeny Osipov

    (Department of Computer Science, Electrical and Space Engineering, Luleå Tekniska Universitet, SE-97187 Luleå, Sweden)

  • Andrew Jennings

    (Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3083, Australia)

  • Valeriy Vyatkin

    (Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland
    Department of Computer Science, Electrical and Space Engineering, Luleå Tekniska Universitet, SE-97187 Luleå, Sweden)

Abstract

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.

Suggested Citation

  • Sachin Kahawala & Daswin De Silva & Seppo Sierla & Damminda Alahakoon & Rashmika Nawaratne & Evgeny Osipov & Andrew Jennings & Valeriy Vyatkin, 2021. "Robust Multi-Step Predictor for Electricity Markets with Real-Time Pricing," Energies, MDPI, vol. 14(14), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4378-:d:597954
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    Cited by:

    1. Jun Dong & Xihao Dou & Aruhan Bao & Yaoyu Zhang & Dongran Liu, 2022. "Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    2. Harri Aaltonen & Seppo Sierla & Ville Kyrki & Mahdi Pourakbari-Kasmaei & Valeriy Vyatkin, 2022. "Bidding a Battery on Electricity Markets and Minimizing Battery Aging Costs: A Reinforcement Learning Approach," Energies, MDPI, vol. 15(14), pages 1-19, July.

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