IIoT-Driven AI for Electrifcation Automation
dc.contributor.author | BOUTAA Ali | |
dc.date.accessioned | 2025-04-27T11:21:13Z | |
dc.date.issued | 2024-06-15 | |
dc.description.abstract | In the midst of the AI era, the industrial domain is witnessing the increasing prominence of AI. This project aims to integrate AI into the Siemens system, taking a significant step towards Industry 4.0. By harnessing the power of AI, we will revolutionize electricity consumption prediction, enabling organizations to make informed decisions and optimize their energy usage. Our strategic approach includes analyzing unique business needs, preparing data, developing AI models, deploying them seamlessly, and continuously monitoring and improving their performance. We will leverage AI techniques such as artificial neural networks, genetic algorithms, and expert systems to transform the energy sector and support the growth and stability of Industry 4.0. This integration will empower organizations to make informed decisions, reduce their carbon footprint, and optimize energy usage, ultimately contributing to a more sustainable future. This project will enhance operational performance and productivity, increase competitiveness in the Industry 4.0 landscape, and pave the way for a more sustainable and technologically advanced industrial sector by streamlining decision-making processes and improving energy efficiency. | |
dc.identifier.citation | BOUTAA Ali, "IIoT-Driven AI for Electrifcation Automation - Predictive Analytics in Siemens Systems for Future Consumption Optimization", Engineer Project, HNS-RE2SD, 2024. | |
dc.identifier.uri | http://dspace.hns-re2sd.dz:4000/handle/123456789/12 | |
dc.language.iso | en | |
dc.subject | Artificial Intelligence | |
dc.subject | IIoT | |
dc.subject | Electrifcation Automation | |
dc.title | IIoT-Driven AI for Electrifcation Automation | |
dc.title.alternative | Predictive Analytics in Siemens Systems for Future Consumption Optimization | |
dc.type | Thesis |
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