Ontology-Based Semantic Retrieval Method of Energy Consumption Management

Xiao, Ya-Qi; Hu, Zhen-Zhong; Lin, Jia-Rui

doi:10.1007/978-3-030-00220-6_28

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Specifically, a systematic scheme supporting high-level data fusion is proposed to promote a better semantic information integration and hence developing a powerful energy efficiency ecosystem. In [61], an ontology method to model information fusion, exchange and further semantic data retrieval is proposed for a better energy management in buildings. In this regard, semantic information collected from multiple data sources and ontologies are then deployed for representing energy consumption states and describing functionalities of a developed energy efficiency system.…”

Section: • L2 Sensor Level Fusionmentioning

confidence: 99%

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Himeur,

Alsalemi,

Al-Kababji

et al. 2020

Preprint

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Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-theart data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored.Keywords Data fusion • energy efficiency • sensors • appliance identification • fusion of 2D descriptors • machine learning.

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Section: • L2 Sensor Level Fusionmentioning

confidence: 99%

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Himeur,

Alsalemi,

Al-Kababji

et al. 2020

Preprint

View full text Add to dashboard Cite

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Knowledge Extraction and Discovery Based on BIM: A Critical Review and Future Directions

Leng

Lin

et al. 2021

Arch Computat Methods Eng

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In the past, knowledge in the fields of Architecture, Engineering and Construction (AEC) industries mainly come from experiences and are documented in hard copies or specific electronic databases. In order to make use of this knowledge, a lot of studies have focused on retrieving and storing this knowledge in a systematic and accessible way. The Building Information Modeling (BIM) technology proves to be a valuable media in extracting data because it provides physical and functional digital models for all the facilities within the life-cycle of the project. Therefore, the combination of the knowledge science with BIM shows great potential in constructing the knowledge map in the field of the AEC industry. Based on literature reviews, this article summarizes the latest achievements in the fields of knowledge science and BIM, in the aspects of (1) knowledge description, (2) knowledge discovery, (3) knowledge storage and management, (4) knowledge inference and (5) knowledge application, to show the state-of-arts and suggests the future directions in the application of knowledge science and BIM technology in the fields of AEC industries. The review indicates that BIM is capable of providing information for knowledge extraction and discovery, by adopting semantic network, knowledge graph and some other related methods. It also illustrates that the knowledge is helpful in the design, construction, operation and maintenance periods of the AEC industry, but now it is only at the beginning stage.

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Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

et al. 2020

View full text Add to dashboard Cite

Ontology-Based Semantic Retrieval Method of Energy Consumption Management

Cited by 3 publications

References 11 publications

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Knowledge Extraction and Discovery Based on BIM: A Critical Review and Future Directions

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

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