发表机构
Universidad Autónoma de Guadalajara; Ulm University; Ulm University of Applied Sciences（瓜达拉哈拉自治大学; 乌尔姆大学; 乌尔姆应用科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究开发了一款成本约65美元的物联网设备，集成多传感器，采用混合架构结合云外训练与设备端增量学习，经现场部署验证，其太阳能预测精度优于气候学基准，可实现低成本分布式环境监测与太阳能预测。
AI 中文摘要
超局地气象感知对精确的太阳能光伏预测至关重要，但专业级气象站的单节点投资极易超过1000美元，使得分布式部署在经济上难以实现。本研究提出一种基于ESP32微控制器的模块化物联网（IoT）设备，集成了温度、湿度、光照度和太阳辐照度传感器，采用IP68级外壳，在德国采购组件时的总硬件成本约为65美元。该系统采用混合架构，将外部模型训练（使用Python软件和开源库TensorFlow在常规计算机上执行）与设备端自主24小时太阳能电压预测（通过具有3011个参数（11.8KB）的三层前馈网络实现）解耦。该网络在现场收集的数据上进行离线训练，以静态权重矩阵形式部署在微控制器上，无需云连接。设备端增量梯度下降机制可在部署后实现连续模型自适应，无需外部重新训练。该系统通过两次现场部署进行评估：在德国乌尔姆进行的短期硬件和固件验证，以及在墨西哥萨波潘进行的115天部署，其中包含84天训练和31天自主运行，零记录缺失。在一个干净的28天白天窗口内，嵌入式模型的决定系数为0.9165，平均绝对误差为0.2975V（占工作范围的4.65%），优于气候学基准（技能分数0.64），但未超过24小时持续性基准。冻结权重 ablation 实验证实，设备端更新机制可产生微小但统计上显著的精度增益（p=0.001），证明自主增量学习在无需云连接的低成本硬件上是可行的。
英文摘要
Hyperlocal meteorological sensing is essential for accurate solar photovoltaic forecasting, yet professional-grade meteorological stations require investments easily exceeding 1000~USD per node, making distributed deployments economically inaccessible. This work presents a modular internet of things (IoT) device based on the ESP32 microcontroller integrating temperature, humidity, luminosity, and solar irradiance sensors in an IP68-rated enclosure at a total hardware costs of about \$65~USD when components are sourced in Germany. A hybrid architecture decouples external model training, performed on a conventional computer using the software Python and the open-source library TensorFlow, from autonomous 24-hour solar voltage forecasting executed on-device via a three-layer feedforward network with 3{,}011 parameters (11.8\,KB). The network is trained offline on site-collected data and deployed on the microcontroller as static weight matrices without cloud connectivity. An on-device incremental gradient descent mechanism enables continuous model adaptation after deployment without external retraining. The system was evaluated through two field deployments: a short period of hardware and firmware validation in Ulm, Germany, and a 115-day deployment in Zapopan, Mexico, comprising 84~days of training and 31~days of autonomous operation with zero missing records. Over a clean 28-day daytime window, the embedded model attained a coefficient of determination of 0.9165 and a mean absolute error of 0.2975~V (4.65\% of the operational range), outperforming a climatology baseline (skill score 0.64) while not surpassing a 24-hour persistence baseline. A frozen-weight ablation confirms that the on-device update mechanism yields a small but statistically robust accuracy gain ($p = 0.001$), demonstrating that autonomous incremental learning is feasible on low-cost hardware without cloud connectivity.
