# Solar forecasting preprint reports lower errors with combined neural models

_Published Wednesday, October 21, 2026 at 12:08 AM EDT · Science, AI · Latest · Tier 2 — Notable_

Researchers report in an arXiv preprint that a hybrid model outperformed statistical, machine learning and deep learning baselines in solar power forecasting tests. The system produces forecasts for 30-minute, 2-hour, 6-hour, 12-hour and 24-hour horizons within one pipeline.

It combines a Temporal Fusion Transformer, which learns short-term changes, with N-BEATS, which captures longer-term trends and seasonal patterns. An attention mechanism adjusts how the two models contribute to predictions.

The researchers report mean absolute percentage errors below 5% across the tested datasets and deployment on a cloud platform for real-time forecasting. Evaluation used multi-year data from the Desert Knowledge Australia Solar Centre.

## Sources

- [cs.LG updates on arXiv.org](https://arxiv.org/abs/2610.27155)

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Canonical: https://techandbusiness.org/newswire/vvkj-jc_z_JALatZF_r9g2
Published: 2026-10-21T04:08:02.255Z
Story chronology: 2026-10-21T04:00:00.000Z
Retrieved: 2026-10-25T06:20:59.394Z
Publisher: Tech & Business (techandbusiness.org)