Recent advances in energy conversion, information technology, the internet, new types of web, and communication technologies have enabled the interconnection of all physical objects, including sensors and actuators. Web-enabled smart objects have paved the way for smart homes by enabling innovative services. In this work, the idea of using time series analysis based on machine learning and forecasting considering the weather conditions is discussed, to enhance automation and improve intelligence. The proposed Prophet model is used to predict the future net energy consumption and generation for improving energy efficiency and enabling power backup. Energy efficiency is the need of the hour, wherein major utilization of energy is in the residential sector, it is gravely important to analyze current generation and consumption and act accordingly for the future predicted results. Moreover, smart homes are dependent on web technologies and telecommunication for the operation of every action, which makes it crucial to have necessary power backup. The Prophet forecasting model, after parameter tuning and logistic growth pattern with additional regressors, gives only 0.27 mean absolute error and 0.13 mean squared error for predicting future energy consumption as compared to other models.

中文翻译：

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一种基于 Hypertuned Prophet 的新型节能方法，适用于支持物联网的智能家居

能源转换、信息技术、互联网、新型网络和通信技术的最新进展使得所有物理对象（包括传感器和执行器）能够互连。支持网络的智能对象通过支持创新服务为智能家居铺平了道路。在这项工作中，讨论了使用基于机器学习的时间序列分析和考虑天气条件的预测来增强自动化和提高智能化的想法。所提出的 Prophet 模型用于预测未来的净能源消耗和发电量，以提高能源效率并实现电力备份。能源效率是当前的需要，其中能源的主要利用是在住宅领域，分析当前的发电和消耗并针对未来的预测结果采取相应的行动非常重要。此外，智能家居的每项操作都依赖于网络技术和电信，这使得必要的备用电源变得至关重要。与其他模型相比，Prophet 预测模型在参数调整和带有附加回归器的逻辑增长模式之后，在预测未来能源消耗方面仅给出 0.27 平均绝对误差和 0.13 均方误差。