Emerging Trends in the use of smart portable accessories, particularly within the context of the Internet of Things (IoT), where smart sensor devices are employed for data gathering, require advancements in energy management mechanisms. This study aims to provide an intelligent energy management mechanism for wearable/portable devices through the use of predictions, monitoring, and analysis of the performance indicators for energy harvesting, majorly focusing on the hybrid PV-wind systems. To design a robust and precise model, prediction algorithms are compared and analysed for an efficient decision support system. Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) prediction algorithms are used to develop a Shallow Neural Network (SNN) for time series prediction. The proposed SNN model uses a closed-loop NARX recurrent dynamic neural network to predict the active power and energy of a hybrid system based on the experimental data of solar irradiation, wind speed, wind direction, humidity, precipitation, ambient temperature and atmospheric pressure collected from Jan 1st 2015 to Dec 26th 2015. The historical hourly metrological data set is established using calibrated sensors deployed at Middle East Technical University (METU), NCC. The accessory considered in this study is called as Smart Umbrella System (SUS), which uses a Raspberry Pi module to fetch the weather data from the current location and store it in the cloud to be processed using SNN classified prediction algorithms. The results obtained show that using the SNN model, it is possible to obtain predictions with 0.004 error rate in a computationally efficient way within 20 s. With the experiments, we are able to observe that for the period of observation, the energy harvested is 178 Wh/d, where the system estimates energy as 176.5 Wh/d, powering the portable accessories accurately.

使用智能便携式配件的新兴趋势，特别是在采用智能传感器设备进行数据收集的物联网（IoT）的背景下，需要对能源管理机制进行改进。这项研究旨在通过使用预测，监视和分析能量收集性能指标来提供可穿戴/便携式设备的智能能源管理机制，主要侧重于混合光伏风系统。为了设计鲁棒而精确的模型，比较并分析了预测算法，以建立有效的决策支持系统。Levenberg-Marquardt（LM），贝叶斯正则化（BR）和缩放共轭梯度（SCG）预测算法用于开发用于时序预测的浅层神经网络（SNN）。所提出的SNN模型使用闭环NARX递归动态神经网络基于收集的太阳辐射，风速，风向，湿度，降水，环境温度和大气压的实验数据来预测混合系统的有功功率和能量从2015年1月1日至2015年12月26日。使用部署在NCC的中东技术大学（METU）的校准传感器建立历史小时计量数据集。本研究中考虑的附件称为智能雨伞系统（SUS），它使用Raspberry Pi模块从当前位置获取天气数据并将其存储在云中，以使用SNN分类的预测算法进行处理。获得的结果表明，使用SNN模型，可以获得0的预测。004错误率以有效的计算方式在20 s内。通过实验，我们可以观察到，在观察期间内，收集的能量为178 Wh / d，系统估计能量为176.5 Wh / d，可为便携式配件提供准确的动力。