# Semi-Supervised Deep Domain Adaptation for Predicting Solar Power Across Different Locations

arXiv: https://arxiv.org/abs/2508.04165 · HTML: https://mlsanity.com/paper/2508.04165/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks](https://mlsanity.com/paper/2004.05937/) (2020)
- [Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives](https://mlsanity.com/paper/2208.07422/) (2022)
- [Algorithms and Theory for Multiple-Source Adaptation](https://mlsanity.com/paper/1805.08727/) (2018)
- [Semi-Supervised Domain Adaptation with Source Label Adaptation](https://mlsanity.com/paper/2302.02335/) (2023)
- [Location Agnostic Source-Free Domain Adaptive Learning to Predict Solar Power Generation](https://mlsanity.com/paper/2401.14422/) (2024)
- [Location Agnostic Adaptive Rain Precipitation Prediction using Deep Learning](https://mlsanity.com/paper/2402.01208/) (2024)
- [Wind Power Prediction across Different Locations using Deep Domain Adaptive Learning](https://mlsanity.com/paper/2405.11188/) (2024)

## Abstract (arXiv)

Accurate solar generation prediction is essential for proper estimation of renewable energy resources across diverse geographic locations. However, geographical and weather features vary from location to location which introduces domain shift - a major bottleneck to develop location-agnostic prediction model. As a result, a machine-learning model which can perform well to predict solar power in one location, may exhibit subpar performance in another location. Moreover, the lack of properly labeled data and storage issues make the task even more challenging. In order to address domain shift due to varying weather conditions across different meteorological regions, this paper presents a semi-supervised deep domain adaptation framework, allowing accurate predictions with minimal labeled data from the target location. Our approach involves training a deep convolutional neural network on a source location's data and adapting it to the target location using a source-free, teacher-student model configuration. The teacher-student model leverages consistency and cross-entropy loss for semi-supervised learning, ensuring effective adaptation without any source data requirement for prediction. With annotation of only 20 % data in the target domain, our approach exhibits an improvement upto 11.36 %, 6.65 %, 4.92% for California, Florida and New York as target domain, respectively in terms of accuracy in predictions with respect to non-adaptive approach.