# Real-time prediction of workplane illuminance distribution for daylight-linked controls using non-intrusive multimodal deep learning

arXiv: https://arxiv.org/abs/2512.14058 · HTML: https://mlsanity.com/paper/2512.14058/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 63 references that are in the map.
- First posted on arXiv 16 Dec 2025.

## Builds on (most cited first, 4 of 4)

- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [Deep Learning for HDR Imaging: State-of-the-Art and Future Trends](https://mlsanity.com/paper/2110.10394/) (2021)
- [AdaptiveAE: An Adaptive Exposure Strategy for HDR Capturing in Dynamic Scenes](https://mlsanity.com/paper/2508.13503/) (2025)

## Abstract (arXiv)

Daylight-linked controls (DLCs) have significant potential for energy savings in buildings, especially when abundant daylight is available and indoor workplane illuminance can be accurately predicted in real time. Most existing studies on indoor daylight predictions were developed and tested for static scenes. This study proposes a multimodal deep learning framework that predicts indoor workplane illuminance distributions in real time from non-intrusive images with temporal-spatial features. By extracting image features only from the side-lit window areas rather than interior pixels, the approach remains applicable in dynamically occupied indoor spaces. A field experiment was conducted in a test room in Guangzhou (China), where 17,344 samples were collected for model training and validation. The model achieved R2 > 0.98 with RMSE < 0.14 on the same-distribution test set and R2 > 0.82 with RMSE < 0.17 on an unseen-day test set, indicating high accuracy and acceptable temporal generalization.