# Ultra-short-term solar power forecasting by deep learning and data reconstruction

arXiv: https://arxiv.org/abs/2509.17095 · HTML: https://mlsanity.com/paper/2509.17095/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 63 references that are in the map.
- First posted on arXiv 21 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?](https://mlsanity.com/paper/2107.06174/) (2021)
- [A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance](https://mlsanity.com/paper/2403.08808/) (2024)
- [Data Sharing, Privacy and Security Considerations in the Energy Sector: A Review from Technical Landscape to Regulatory Specifications](https://mlsanity.com/paper/2503.03539/) (2025)
- [Privacy-preserving transactive energy systems: Key topics and open research challenges](https://mlsanity.com/paper/2312.11564/) (2023)
- [Active learning for anti-disturbance dual control of unknown nonlinear systems](https://mlsanity.com/paper/2212.08934/) (2022)
- [Adaptive robust tracking control with active learning for linear systems with ellipsoidal bounded uncertainties](https://mlsanity.com/paper/2308.03727/) (2023)
- [Ensemble Control for Stochastic Systems with Asymmetric Laplace Noises](https://mlsanity.com/paper/2405.09973/) (2024)
- [Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation](https://mlsanity.com/paper/2502.05069/) (2025)

## Built on by (most cited first, 2 of 2)

- [Exploring the Interpretability of Forecasting Models for Energy Balancing Market](https://mlsanity.com/paper/2602.00049/) (2026)
- [Urban Power Grid Topology and Hierarchy Identification from Open Data](https://mlsanity.com/paper/2606.21352/) (2026)

## Abstract (arXiv)

The integration of solar power has been increasing as the green energy transition rolls out. The penetration of solar power challenges the grid stability and energy scheduling, due to its intermittent energy generation. Accurate and near real-time solar power prediction is of critical importance to tolerant and support the permeation of distributed and volatile solar power production in the energy system. In this paper, we propose a deep-learning based ultra-short-term solar power prediction with data reconstruction. We decompose the data for the prediction to facilitate extensive exploration of the spatial and temporal dependencies within the data. Particularly, we reconstruct the data into low- and high-frequency components, using ensemble empirical model decomposition with adaptive noise (CEEMDAN). We integrate meteorological data with those two components, and employ deep-learning models to capture long- and short-term dependencies towards the target prediction period. In this way, we excessively exploit the features in historical data in predicting a ultra-short-term solar power production. Furthermore, as ultra-short-term prediction is vulnerable to local optima, we modify the optimization in our deep-learning training by penalizing long prediction intervals. Numerical experiments with diverse settings demonstrate that, compared to baseline models, the proposed method achieves improved generalization in data reconstruction and higher prediction accuracy for ultra-short-term solar power production.