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 (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,并进一步实现能源可持续性,如果在设备颗粒处提供电能消耗和生产的数据,则最终用户能够更深入地了解系统的细节,如果能够以设备颗粒度提供能源,则可以进一步实现能源可持续性。然而,这个领域的许多数据库是从其他领域,包括纯粹与能源有关的信息,从其他领域分散而来,这个领域的许多数据库都来自其他领域,这可能导致丧失关于每个设备能源使用的信息(例如天气),同时,在机器学习模型等计算模型技术中广泛使用大量这类数据集。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的准确性和性,但模型的可靠性是无法保证的,因为这种模型在考虑信息遗漏时很容易受到数据输入波动的影响。这篇文章涉及智能能源系统领域的数据孤立问题,通过审查家庭能源系统顶部的Semmanic Web 方法,我们提供了一种基于理论的方法,用以管理设备级分辨率的分散式数据。因此,在网络上可以轻易地提供与每个设备相关的数据的范围,从而在网络上进行网络上可变换的数据,从而可以轻易地从网络上提供。