An intelligent system for energy management in smart cities based on big data and ontology

Sayah, Zaoui; Kazar, Okba; Lejdel, Brahim; Laouid, Abdelkader; Ghenabzia, Ahmed

doi:10.1108/sasbe-07-2019-0087

Cited by 28 publications

(17 citation statements)

References 41 publications

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“…In addition, to answer RQ6, the following issues can be considered. RQ6: What challenges, open issues, and future paths were identified by paying attention to the big data handling approaches in smart cities to be considered as a subsequent tendency?In recent years, green smart cities have been targeted by decision‐makers due to carbon footprint, energy consumption in smart cities that should be reduced 121 . Furthermore, energy is one of the essential issues that scholars consider a bit for big data handling approaches in smart cities.…”

Section: Open Issues and Future Trendsmentioning

confidence: 99%

Leveraging big data in smart cities: A systematic review

Karimi

Kashani

Akbari

et al. 2021

Concurrency and Computation

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Recently, the notion of a smart city, which includes smart well‐being, smart transit, and smart society, has attracted much attention due to its impact on people's quality of living. Data in smart cities are characterized by variety, velocity, volume, value, and veracity that are the well‐known characteristics of big data. The fast pace expanding of IoT devices and sensors in smart cities generates a huge volume of data that can help decision‐makers and managers in city management. The aim of this article is to wholly and systematically review big data handling approaches in smart cities, in which we analyze research efforts published between 2013 and February 2021, where these techniques are categorized based on their algorithms and architectures. Further, the main ideas, evaluation techniques, tools, evaluation metrics, algorithm types, advantages, and disadvantages are explored. Additionally, essential evaluation factors are introduced in which scalability and availability by 16%, time by 15% and accuracy by 11% are more in focus, and finally, some of the challenges, open issues, and future trends that are valuable for further research are suggested in big data handling approaches in smart cities.

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Section: Open Issues and Future Trendsmentioning

confidence: 99%

Leveraging big data in smart cities: A systematic review

Karimi

Kashani

Akbari

et al. 2021

Concurrency and Computation

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“…Other core ontologies within the AEC domain focus on different types of constructions such as bridges, smart buildings, and cities (Sayah, Kazar, Lejdel, Laouid, & Ghenabzia, 2020). For example, the Bridge Topology Ontology (BROT) is a core ontology for defining the bridge constructions, including aggregated zones and components and their topological relations.…”

Section: Aec Domain Ontologiesmentioning

confidence: 99%

Application of semantic Web ontologies for the improvement of information exchange in existing buildings

Sadeghineko

Kumar

2021

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Purpose Facilitating the information exchange and interoperability between stakeholders during the life-cycle of an asset can be one of the fundamental necessities for developing an enhanced information exchange framework. Such a framework can also improve the successful accomplishment of building projects. This paper aims to use Semantic Web technologies for facilitating information exchange within existing building projects. Design/methodology/approach In real-world building projects, the construction industry’s information supply chain may initiate from near scratch when new building projects are started resulting in diverse data structures represented in unstructured data sources, like Excel spreadsheets and documents. Large-scale data generated throughout a building's life-cycle requires exchanging and processing during an asset's Operation and Maintenance (O&M) phase. Building information modelling (BIM) processes and related technologies can address some of the challenges and limitations of information exchange and interoperability within new building projects. However, the use of BIM in existing and retrofit assets has been hampered by the challenges surrounding the limitations of existing technologies. Findings The aim of this paper is twofold. Firstly, it briefly outlines the framework previously developed for generating semantically enriched 3D retrofit models. Secondly, a framework is proposed focussing on facilitating the information exchange and interoperability for existing buildings. Semantic Web technologies and standards, such as Web Ontology Language and existing AEC domain ontologies are used to enhance and improve the proposed framework. Originality/value The proposed framework is evaluated by implementing an example application and the Resource Description Framework data produced by the previously developed framework. The proposed approach makes a valuable contribution to the asset/facilities management (AM/FM) domain. It should be of interest to various FM practices for existing assets, such as the building information/knowledge management for design, construction and O&M stages of an asset’s life-cycle.

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“…More recently, the development of strategies that require the participation of the construction industry such as smart cities whose objective is "to increase the quality of life in multiple dimensions, such as health, transport, energy efficiency, etc." Naoui et al (2020) and Sayah et al (2020) noted that BD is used to manage and analyse its database collected from numerous and diverse sources, in huge quantities and most cases in real-time. Activities such as the reduction of energy consumption are one of the main goals of governments and a focal point of both the construction industry, the expansion of smart cities and sustainability, which can be achieved by the application of BD by allowing the cooperation of various technologies that together can provide energy efficiency than each technology in its own.…”

Section: Big Data In Constructionmentioning

confidence: 99%

Awareness of Big Data concept in the Dominican Republic construction industry: an empirical study

Renukappa

Suresh

Reyes-Veras

2021

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Purpose The construction industry, being one of the main activities in the ever-demanding need for technology developments, sometimes falls short of other industries in terms of implementation. The adoption of Big Data (BD) in industries such as health and retail has had positive impacts in aspects such as decision-making processes and forecasting trends that allow planning some future business movements. Hence, the question of whether these results can be imitated in the construction industry. Therefore, this paper aims to address the level of awareness identified as a first step towards implementation of the BD concept within the construction industry in the Dominican Republic (DR). Design/methodology/approach As little to no information exist on the subject; the selected approach to perform this research was qualitative methodology; 21 semi-structured interviews were studied using situational awareness. Four levels of awareness were developed based on the Endsley’s Situation Awareness model. Findings The results showed that nearly 95% of the interviewees had either no knowledge or very basic awareness of the BD requirements or intermediate awareness, but only 5% had applied BD concepts in the construction industry. Originality/value This study shows the gaps that exist in the understanding and implementation of BD concepts in the DR construction industry. This paper establishes the need to develop continuous professional development programmes for construction professionals and a need to update curriculum in construction-related education.

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An intelligent system for energy management in smart cities based on big data and ontology

Cited by 28 publications

References 41 publications

Leveraging big data in smart cities: A systematic review

Leveraging big data in smart cities: A systematic review

Application of semantic Web ontologies for the improvement of information exchange in existing buildings

Awareness of Big Data concept in the Dominican Republic construction industry: an empirical study

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