用非侵入式图像实时预测室内光照分布，提升智能照明节能效果
Real-time prediction of workplane illuminance distribution for daylight-linked controls using non-intrusive multimodal deep learning
- 从侧窗区域提取图像特征，适配动态人员场景
- 同分布测试下决定系数超0.98，误差低于0.14
- 适合建筑节能系统研发与智能照明部署
日光联动控制（DLC）在建筑节能中潜力巨大，尤其当可精准实时预测室内工作面照度时。现有研究多针对静态场景，本研究提出一种多模态深度学习框架，仅通过非侵入式图像的时空特征，实时预测室内工作面照度分布。方法聚焦于侧窗区域图像特征提取，避免依赖室内像素，适用于动态占用空间。在广州某实验房间开展实地测试，共收集17,344组样本用于模型训练与验证。模型在同分布测试集上达到R² > 0.98，RMSE < 0.14；在未见日期测试集上R² > 0.82，RMSE < 0.17，表明具有高精度和可接受的时间泛化能力。
原文摘要 · Abstract (English)
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.
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