In a decentralized household energy system comprised of various devices such as home appliances, electric vehicles, and solar panels, end-users are able to dig deeper into the system's details and further achieve energy sustainability if they are presented with data on the electric energy consumption and production at the granularity of the device. However, many databases in this field are siloed from other domains, including solely information pertaining to energy. This may result in the loss of information (\textit{e.g.} weather) on each device's energy use. Meanwhile, a large number of these datasets have been extensively used in computational modeling techniques such as machine learning models. While such computational approaches achieve great accuracy and performance by concentrating only on a local view of datasets, model reliability cannot be guaranteed since such models are very vulnerable to data input fluctuations when information omission is taken into account. This article tackles the data isolation issue in the field of smart energy systems by examining Semantic Web methods on top of a household energy system. We offer an ontology-based approach for managing decentralized data at the device-level resolution in a system. As a consequence, the scope of the data associated with each device may easily be expanded in an interoperable manner throughout the Web, and additional information, such as weather, can be obtained from the Web, provided that the data is organized according to W3C standards.
翻译:在由家用电器、电动车辆和太阳能电池板等各种装置组成的分散式家庭能源系统中,终端用户能够更深入地了解系统的细节,进一步实现能源可持续性,如果在设备颗粒度上提供电力能源消耗和生产的数据,则最终用户能够更深入地了解系统的细节,并获得能源可持续性。然而,这个领域的许多数据库都从其他领域,包括仅与能源有关的信息,被分散在外,但有可能造成关于每个装置能源使用的信息(textit{e.e.wed)天气)的丢失。与此同时,大量这类数据集被广泛用于计算模型技术,如机器学习模型。虽然这种计算方法通过只注重对数据集的当地观点而实现高度准确性和性,但模型可靠性却无法保证,因为考虑到信息遗漏时,这类模型极易受到数据输入波动的影响。这篇文章探讨了智能能源系统领域的数据孤立问题,审查了家庭能源系统之上的Smantic网络方法。我们提供了一种基于网络基础的方法,用以管理设备级分辨率的分散数据。在系统内管理设备级分辨率时,我们提供了一种管理基于网络间数据的基于网络的基于网络的基于可操作性的方法。因此,在网络上可以轻易地扩大数据的范围,从网络上提供一种可操作性的额外数据,从而在网络上提供一种可操作性的数据,从而在网络上可以轻易地扩大到网络上提供一种可操作性的数据。