An improved iTransformer and BiLSTM hybrid model improves the accuracy of short term electricity price forecasting for the Australian National Electricity Market
Jing Lan; Xuguang Zhao; Yongfeng Zhao
- Published
- Publisher
- Springer Science and Business Media LLC
- Journal / collection
- Discover Artificial Intelligence
- Research area
- Energy & infrastructure
- Source type
- Journal article
- DOI
- 10.1007/s44163-026-01705-2
The original publisher provides the full text and its access options.
Cite this source
Jing Lan; Xuguang Zhao; Yongfeng Zhao. An improved iTransformer and BiLSTM hybrid model improves the accuracy of short term electricity price forecasting for the Australian National Electricity Market. Discover Artificial Intelligence. 2026-07-09. https://doi.org/10.1007/s44163-026-01705-2