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 and Valeriy Vyatkin
Additional contact information
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

Energies, 2021, vol. 14, issue 14, 1-20

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.

Keywords: demand response; real-time pricing; prosumers; electricity price forecasting; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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