Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Khosrojerdi, F.; Akhigbe, O.; Gagnon, S.; Ramirez, A.; Richards, G.
Integrating artificial intelligence and analytics in smart grids: a systematic literature review Article de journal
Dans: International Journal of Energy Sector Management, vol. 16, no 2, p. 318–338, 2022.
Résumé | Liens | BibTeX | Étiquettes: Advanced Analytics, Automation, Building energy consumption, Data Analytics, Design/methodology/approach, Dynamic energy managements, Electric power system control, Electric power transmission networks, Energy management, Energy management systems, Energy utilization, Extract, Home energy management systems, Information management, Intelligent systems, Project management, quality control, Real time systems, SCADA systems, Smart power grids, Solar buildings, Supervisory control and dataacquisition systems (SCADA), System stability, Systematic literature review, transform and loads, Voltage stability assessment
@article{khosrojerdi_integrating_2022,
title = {Integrating artificial intelligence and analytics in smart grids: a systematic literature review},
author = {F. Khosrojerdi and O. Akhigbe and S. Gagnon and A. Ramirez and G. Richards},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112559126&doi=10.1108%2fIJESM-06-2020-0011&partnerID=40&md5=7052f94c993368405955c1d33d87043c},
doi = {10.1108/IJESM-06-2020-0011},
year = {2022},
date = {2022-01-01},
journal = {International Journal of Energy Sector Management},
volume = {16},
number = {2},
pages = {318–338},
abstract = {Purpose: The purpose of this study is to explore the latest approaches in integrating artificial intelligence and analytics (AIA) in energy smart grid projects. Empirical results are synthesized to highlight their relevance from a technology and project management standpoint, identifying several lessons learned that can be used for planning highly integrated and automated smart grid projects. Design/methodology/approach: A systematic literature review leads to selecting 108 research articles dealing with smart grids and AIA applications. Keywords are based on the following research questions: What is the growth trend in Smart Grid projects using intelligent systems and data analytics? What business value is offered when AI-based methods are applied? How do applications of intelligent systems combine with data analytics? What lessons can be learned for Smart Grid and AIA projects? Findings: The 108 selected articles are classified according to the following four research issues in smart grids project management: AIA integrated applications; AI-focused technologies; analytics-focused technologies; architecture and design methods. A broad set of smart grid functionality is reviewed, seeking to find commonality among several applications, including as follows: dynamic energy management; automation of extract, transform and load for Supervisory Control And Data Acquisition (SCADA) systems data; multi-level representations of data; the relationship between the standard three-phase transforms and modern data analytics; real-time or short-time voltage stability assessment; smart city architecture; home energy management system; building energy consumption; automated fault and disturbance analysis; and power quality control. Originality/value: Given the diversity of issues reviewed, a more capability-focused research agenda is needed to further synthesize empirical findings for AI-based smart grids. Research may converge toward more focus on business rules systems, that may best support smart grid design, proof development, governance and effectiveness. These AIA technologies must be further integrated with smart grid project management methodologies and enable a greater diversity of renewable and non-renewable production sources. © 2021, Emerald Publishing Limited.},
keywords = {Advanced Analytics, Automation, Building energy consumption, Data Analytics, Design/methodology/approach, Dynamic energy managements, Electric power system control, Electric power transmission networks, Energy management, Energy management systems, Energy utilization, Extract, Home energy management systems, Information management, Intelligent systems, Project management, quality control, Real time systems, SCADA systems, Smart power grids, Solar buildings, Supervisory control and dataacquisition systems (SCADA), System stability, Systematic literature review, transform and loads, Voltage stability assessment},
pubstate = {published},
tppubtype = {article}
}
Purpose: The purpose of this study is to explore the latest approaches in integrating artificial intelligence and analytics (AIA) in energy smart grid projects. Empirical results are synthesized to highlight their relevance from a technology and project management standpoint, identifying several lessons learned that can be used for planning highly integrated and automated smart grid projects. Design/methodology/approach: A systematic literature review leads to selecting 108 research articles dealing with smart grids and AIA applications. Keywords are based on the following research questions: What is the growth trend in Smart Grid projects using intelligent systems and data analytics? What business value is offered when AI-based methods are applied? How do applications of intelligent systems combine with data analytics? What lessons can be learned for Smart Grid and AIA projects? Findings: The 108 selected articles are classified according to the following four research issues in smart grids project management: AIA integrated applications; AI-focused technologies; analytics-focused technologies; architecture and design methods. A broad set of smart grid functionality is reviewed, seeking to find commonality among several applications, including as follows: dynamic energy management; automation of extract, transform and load for Supervisory Control And Data Acquisition (SCADA) systems data; multi-level representations of data; the relationship between the standard three-phase transforms and modern data analytics; real-time or short-time voltage stability assessment; smart city architecture; home energy management system; building energy consumption; automated fault and disturbance analysis; and power quality control. Originality/value: Given the diversity of issues reviewed, a more capability-focused research agenda is needed to further synthesize empirical findings for AI-based smart grids. Research may converge toward more focus on business rules systems, that may best support smart grid design, proof development, governance and effectiveness. These AIA technologies must be further integrated with smart grid project management methodologies and enable a greater diversity of renewable and non-renewable production sources. © 2021, Emerald Publishing Limited.
2.
Valverde, R.; Torres, B.; Motaghi, H.
IGI Global, 2018, ISBN: 978-1-5225-5220-8 1-5225-5219-7 978-1-5225-5219-2, (Publication Title: Quantum-Inspired Intelligent Systems for Multimedia Data Analysis).
Résumé | Liens | BibTeX | Étiquettes: Architecture-based, Computer anxiety, Data Analytics, Data collection mechanism, Electro-encephalogram (EEG), Electroencephalography, Information management, Learning management system, Quantum modeling, Surveys, Usability evaluation, Usability testing
@book{valverde_quantum_2018,
title = {A quantum NeuroIS data analytics architecture for the usability evaluation of learning management systems},
author = {R. Valverde and B. Torres and H. Motaghi},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049542254&doi=10.4018%2f978-1-5225-5219-2.ch009&partnerID=40&md5=8bc6e485175501b38a109ed03be0bedb},
doi = {10.4018/978-1-5225-5219-2.ch009},
isbn = {978-1-5225-5220-8 1-5225-5219-7 978-1-5225-5219-2},
year = {2018},
date = {2018-01-01},
publisher = {IGI Global},
abstract = {NeuroIS uses tools such as electroencephalogram (EEG) that can be used to measure high brainwave frequencies that can be linked to human anxiety. Past research showed that computer anxiety influences how users perceive ease of use of a learning management system (LMS). Although computer anxiety has been used successfully to evaluate the usability of LMS, the main data collection mechanisms proposed for its evaluation have been questionnaires. Questionnaires suffer from possible problems such as being inadequate to understand some forms of information such as emotions and honesty in the responses. Quantum-based approaches to consciousness have been very popular in the last years including the quantum model reduction in microtubules of Penrose and Hameroff (1995). The objective of the chapter is to propose an architecture based on a NeuroIS that collects data by using EEG from users and then use the collected data to perform analytics by using a quantum consciousness model proposed for computer anxiety measurements for the usability testing of a LMS. © 2018, IGI Global. All rights reserved.},
note = {Publication Title: Quantum-Inspired Intelligent Systems for Multimedia Data Analysis},
keywords = {Architecture-based, Computer anxiety, Data Analytics, Data collection mechanism, Electro-encephalogram (EEG), Electroencephalography, Information management, Learning management system, Quantum modeling, Surveys, Usability evaluation, Usability testing},
pubstate = {published},
tppubtype = {book}
}
NeuroIS uses tools such as electroencephalogram (EEG) that can be used to measure high brainwave frequencies that can be linked to human anxiety. Past research showed that computer anxiety influences how users perceive ease of use of a learning management system (LMS). Although computer anxiety has been used successfully to evaluate the usability of LMS, the main data collection mechanisms proposed for its evaluation have been questionnaires. Questionnaires suffer from possible problems such as being inadequate to understand some forms of information such as emotions and honesty in the responses. Quantum-based approaches to consciousness have been very popular in the last years including the quantum model reduction in microtubules of Penrose and Hameroff (1995). The objective of the chapter is to propose an architecture based on a NeuroIS that collects data by using EEG from users and then use the collected data to perform analytics by using a quantum consciousness model proposed for computer anxiety measurements for the usability testing of a LMS. © 2018, IGI Global. All rights reserved.