A Local-Temporal Convolutional Transformer for Day-Ahead Electricity Wholesale Price Forecasting

Bowen Zhang, Hongda Tian (), Adam Berry and A. Craig Roussac
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Bowen Zhang: Data Science Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo 2007, Australia
Hongda Tian: Data Science Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo 2007, Australia
Adam Berry: Human Technology Institute, University of Technology Sydney, Sydney 2007, Australia
A. Craig Roussac: Buildings Alive Pty Ltd., Sydney 2000, Australia

Sustainability, 2025, vol. 17, issue 12, 1-22

Abstract: Accurate electricity wholesale price (EWP) forecasting is crucial for advancing sustainability in the energy sector, as it supports more efficient utilization and integration of renewable energy by informing when and how it should be consumed, dispatched, curtailed, or stored. However, high fluctuations in EWP, often resulting from demand–supply imbalances typically caused by sudden surges in electricity usage and the intermittency of renewable energy generation, and unforeseen external events, pose a challenge for accurate forecasting. Incorporating local temporal information (LTI) in time series, such as hourly price changes, is essential for accurate EWP forecasting, as it helps detect rapid market shifts. However, existing methods remain limited in capturing LTI, either relying on point-wise input sequences or, for fixed-length, non-overlapping segmentation methods, failing to effectively model dependencies within and across segments. This paper proposes the Local-Temporal Convolutional Transformer (LT-Conformer) model for day-ahead EWP forecasting, which addresses the challenge of capturing fine-grained LTI using Local-Temporal 1D Convolution and incorporates two attention modules to capture global temporal dependencies (e.g., daily price trends) and cross-feature dependencies (e.g., solar output influencing price). An initial evaluation in the Australian market demonstrates that LT-Conformer outperforms existing state-of-the-art methods and exhibits adaptability in forecasting EWP under volatile market conditions.

Keywords: electricity wholesale price forecasting; energy sustainability; local temporal dependencies; convolutional neural network; transformer (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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