# Wind Power Prediction across Different Locations using Deep Domain Adaptive Learning

arXiv: https://arxiv.org/abs/2405.11188 · HTML: https://mlsanity.com/paper/2405.11188/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 18 May 2024.

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

- [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)

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

- [Cross-Domain Offshore Wind Power Forecasting: Transfer Learning Through Meteorological Clusters](https://mlsanity.com/paper/2601.19674/) (2026)
- [Semi-Supervised Deep Domain Adaptation for Predicting Solar Power Across Different Locations](https://mlsanity.com/paper/2508.04165/) (2025)

## Abstract (arXiv)

Accurate prediction of wind power is essential for the grid integration of this intermittent renewable source and aiding grid planners in forecasting available wind capacity. Spatial differences lead to discrepancies in climatological data distributions between two geographically dispersed regions, consequently making the prediction task more difficult. Thus, a prediction model that learns from the data of a particular climatic region can suffer from being less robust. A deep neural network (DNN) based domain adaptive approach is proposed to counter this drawback. Effective weather features from a large set of weather parameters are selected using a random forest approach. A pre-trained model from the source domain is utilized to perform the prediction task, assuming no source data is available during target domain prediction. The weights of only the last few layers of the DNN model are updated throughout the task, keeping the rest of the network unchanged, making the model faster compared to the traditional approaches. The proposed approach demonstrates higher accuracy ranging from 6.14% to even 28.44% compared to the traditional non-adaptive method.