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

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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.

History

Publication Date

02/07/2021

Journal

Energies

Volume

14

Issue

14

Article Number

4378

Pagination

20p.

Publisher

MDPI

ISSN

1996-1073

Rights Statement

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/).