# Location Agnostic Source-Free Domain Adaptive Learning to Predict Solar Power Generation

arXiv: https://arxiv.org/abs/2401.14422 · HTML: https://mlsanity.com/paper/2401.14422/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 31 references that are in the map.
- First posted on arXiv 24 Jan 2024.

## Builds on (most cited first, 2 of 2)

- [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)

## Built on by (most cited first, 4 of 4)

- [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)
- [Comparative Evaluation of Weather Forecasting using Machine Learning Models](https://mlsanity.com/paper/2402.01206/) (2024)
- [Semi-Supervised Deep Domain Adaptation for Predicting Solar Power Across Different Locations](https://mlsanity.com/paper/2508.04165/) (2025)

## Abstract (arXiv)

The prediction of solar power generation is a challenging task due to its dependence on climatic characteristics that exhibit spatial and temporal variability. The performance of a prediction model may vary across different places due to changes in data distribution, resulting in a model that works well in one region but not in others. Furthermore, as a consequence of global warming, there is a notable acceleration in the alteration of weather patterns on an annual basis. This phenomenon introduces the potential for diminished efficacy of existing models, even within the same geographical region, as time progresses. In this paper, a domain adaptive deep learning-based framework is proposed to estimate solar power generation using weather features that can solve the aforementioned challenges. A feed-forward deep convolutional network model is trained for a known location dataset in a supervised manner and utilized to predict the solar power of an unknown location later. This adaptive data-driven approach exhibits notable advantages in terms of computing speed, storage efficiency, and its ability to improve outcomes in scenarios where state-of-the-art non-adaptive methods fail. Our method has shown an improvement of 10.47 %, 7.44 %, 5.11% in solar power prediction accuracy compared to best performing non-adaptive method for California (CA), Florida (FL) and New York (NY), respectively.