In a decentralized household energy system consisting of various devices such as washing machines, heat pumps, and solar panels, understanding the electric energy consumption and production data at the granularity of the device helps end-users be closer to the system and further achieve the sustainability of energy use. However, many datasets in this area are isolated from other domains with records of only energy-related data. This may raise a loss of information (e.g. weather) that is relevant to the energy use of each device. A noticeable disadvantage is that many of those datasets have to be used in computational modeling approaches such as machine learning models, which are vulnerable to the data feed, to advance the understanding of energy consumption and production. Although such computational methods have achieved a high benchmark merely through a local view of datasets, the reusability cannot be firmly guaranteed when the information omission is taken into account. This paper addresses the data isolation problem in the smart energy systems area by exploring Semantic Web techniques on top of a household energy system. We propose an ontology modeling solution for the management of decentralized data at the resolution of a device in the system. As a result, the scope of the data concerning each device can be easily extended to be wider across the web and more information that may be of interest such as weather can be retrieved from the Web if the data are structured by the ontology.
翻译:在由洗衣机、热泵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,了解该装置颗粒的电能消耗和生产数据有助于终端用户更接近该系统,并进一步实现能源使用的可持续性。然而,这一领域的许多数据集与仅记录与能源有关的数据的其他领域隔绝,这可能造成与每个装置的能源使用相关的信息(如天气)丢失。一个明显的不利之处是,许多这类数据集必须用于计算模型方法,例如机器学习模型,这些模型容易受数据反馈的影响,以增进对能源消费和生产的了解。虽然这类计算方法仅通过对数据集的当地观点就达到了一个高基准,但在考虑信息遗漏时无法牢固地保证重新使用。本文探讨智能能源系统区域的数据孤立问题,在家庭能源系统顶部探索Smantic网络技术。我们提议,在系统设备分辨率上管理分散式数据时,必须使用一个在线模型,以便较容易地从网络结构化的数据范围扩大到网络结构,这样,就能通过网络结构化的每个数据更容易地在网络上获取。