Development of new ontological solution for an energy intelligent management in Adrar city

Saba, Djamel; Sahli, Youcef; Abanda, Fonbeyin Henry; Maouedj, Rachid; Tidjar, Boudjemaa

doi:10.1016/j.suscom.2019.01.009

Cited by 17 publications

(13 citation statements)

References 25 publications

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“…The ontology-based semantic approach has improved the interoperability between the applications. The ontological solution uses internal and external knowledge about environment, behaviour and activities [82]. One of the key motivations for applying ontologies is to ensure heterogeneous sensors can be integrated effectively during natural disasters and crises [83,84].…”

Section: Literature Reviewmentioning

confidence: 99%

Explainable Artificial Intelligence for Developing Smart Cities Solutions

et al. 2020

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Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results.

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Section: Literature Reviewmentioning

confidence: 99%

Explainable Artificial Intelligence for Developing Smart Cities Solutions

et al. 2020

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“…The integrated Ontology offers a generic model to allow logical inference and a data mining classification algorithm, which used to obtain the rules representing normal energy consumptions (Lork et al , 2019). Otherwise, (Saba et al , 2019), based on the same parameters, as well as the occupants’ behavior and activities, used OWL and SWRL for knowledge presentation and intelligent reasoning to reduce energy consumption in smart cities.…”

Section: Related Workmentioning

confidence: 99%

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

Sayah

Kazar

Lejdel³

et al. 2020

SASBE

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PurposeThis research paper aims at proposing a framework based on semantic integration in Big Data for saving energy in smart cities. The presented approach highlights the potential opportunities offered by Big Data and ontologies to reduce energy consumption in smart cities.Design/methodology/approachThis study provides an overview of semantics in Big Data and reviews various works that investigate energy saving in smart homes and cities. To reach this end, we propose an efficient architecture based on the cooperation between ontology, Big Data, and Multi-Agent Systems. Furthermore, the proposed approach shows the strength of these technologies to reduce energy consumption in smart cities.FindingsThrough this research, we seek to clarify and explain both the role of Multi-Agent System and ontology paradigms to improve systems interoperability. Indeed, it is useful to develop the proposed architecture based on Big Data. This study highlights the opportunities offered when they are combined together to provide a reliable system for saving energy in smart cities.Practical implicationsThe significant advancement of contemporary applications (smart cities, social networks, health care, IoT, etc.) requires a vast emergence of Big Data and semantics technologies in these fields. The obtained results provide an improved vision of energy-saving and environmental protection while keeping the inhabitants’ comfort.Originality/valueThis work is an efficient contribution that provides more comprehensive solutions to ontology integration in the Big Data environment. We have used all available data to reduce energy consumption, promote the change of inhabitant’s behavior, offer the required comfort, and implement an effective long-term energy policy in a smart and sustainable environment.

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“…The smart home is sometimes seen as an extension of the concept of the smart grid [64] From this point of view, the main objective of the smart home is to optimize energy consumption by taking into account various inputs such as the mode of use and the real-time presence of residents, the external environment (for weather conditions) [9].…”

Section: Fig 2 Applications Of Artificial Intelligencementioning

confidence: 99%

“…For the design and implementation of projects based on AI technology, several approaches are necessary in the realization, such as the domain ontology which is used as a representative form of knowledge about a world or a certain part of this world [9,10]. They also describe [10]: individuals (basic objects), classes (sets, collections, or types of objects), attributes (properties, functionalities, characteristics or parameters that objects can possess and share), relationships (the links that objects can have them), events (changes to attributes or relationships), meta-class (collections of classes that share certain characteristics) [11].…”

Section: Introductionmentioning

confidence: 99%