The Awareness and Adoption of Artificial Intelligence for Effective Facilities Management in the Energy Sector
Digitalization and artificial intelligence (AI) have infiltrated most sectors of the economy, including the energy sector, where they have been extensively investigated. The aim of the study is primarily to assess the awareness of AI in facility management, and to identify the prospects and challen...
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| Format: | Article |
| Language: | English |
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UJ Press
2021-12-01
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| Series: | Journal of Digital Food, Energy & Water Systems |
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| Online Access: | https://journals.uj.ac.za/index.php/DigitalFoodEnergy_WaterSystems/article/view/718 |
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| _version_ | 1841555022745174016 |
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| author | Jonathan Oluwapelumi Mobayo Ayooluwa Femi Aribisala Saheed Olanrewaju Yusuf Usman Belgore |
| author_facet | Jonathan Oluwapelumi Mobayo Ayooluwa Femi Aribisala Saheed Olanrewaju Yusuf Usman Belgore |
| author_sort | Jonathan Oluwapelumi Mobayo |
| collection | DOAJ |
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Digitalization and artificial intelligence (AI) have infiltrated most sectors of the economy, including the energy sector, where they have been extensively investigated. The aim of the study is primarily to assess the awareness of AI in facility management, and to identify the prospects and challenges of the adoption of AI in the energy sector. The study adopted the quantitative methodology approach, using a structured questionnaire to a sample size of 384 respondents. The questionnaire was administered to professionals such as mechanical, civil, electrical, computer, and mechatronics engineers, and project managers within the North-central geopolitical zone of Nigeria. Data gathered was analysed using descriptive analysis (mean value, weighted total, and relative importance index). The study based on findings concludes that there exists high awareness level about the concept of AI in the energy sector. However, regarding the awareness about some selected AI technologies, machine & deep learning, robotics, and speech recognition had high awareness level. The study also concludes that improved energy management, efficiency and transparency, remote reading of energy meters, and improved planning, operation & control of power systems were prevalent prospects of AI adoption. The major challenging factors to the adoption of AI in the Nigerian energy sector are outdated power system infrastructure, cellular technologies, lack of qualified experts and data science skills, and growing threat from cyber-attacks. The study recommends improved awareness and technical know-how of energy sector personnel, and provision of adequate power system infrastructure to provide stable power supply.
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| format | Article |
| id | doaj-art-8546ab802c2140b1bf435d53cdf30e08 |
| institution | Kabale University |
| issn | 2709-4510 2709-4529 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | UJ Press |
| record_format | Article |
| series | Journal of Digital Food, Energy & Water Systems |
| spelling | doaj-art-8546ab802c2140b1bf435d53cdf30e082025-01-08T06:19:32ZengUJ PressJournal of Digital Food, Energy & Water Systems2709-45102709-45292021-12-012210.36615/digitalfoodenergywatersystems.v2i2.718The Awareness and Adoption of Artificial Intelligence for Effective Facilities Management in the Energy SectorJonathan Oluwapelumi Mobayo0Ayooluwa Femi Aribisala1Saheed Olanrewaju Yusuf2Usman Belgore3Department of Project Management, Federal University of Technology, MinnaDepartment of Project Management, Federal University of Technology, MinnaDepartment of Project Management, Federal University of Technology, MinnaDepartment of Project Management, Federal University of Technology, Minna Digitalization and artificial intelligence (AI) have infiltrated most sectors of the economy, including the energy sector, where they have been extensively investigated. The aim of the study is primarily to assess the awareness of AI in facility management, and to identify the prospects and challenges of the adoption of AI in the energy sector. The study adopted the quantitative methodology approach, using a structured questionnaire to a sample size of 384 respondents. The questionnaire was administered to professionals such as mechanical, civil, electrical, computer, and mechatronics engineers, and project managers within the North-central geopolitical zone of Nigeria. Data gathered was analysed using descriptive analysis (mean value, weighted total, and relative importance index). The study based on findings concludes that there exists high awareness level about the concept of AI in the energy sector. However, regarding the awareness about some selected AI technologies, machine & deep learning, robotics, and speech recognition had high awareness level. The study also concludes that improved energy management, efficiency and transparency, remote reading of energy meters, and improved planning, operation & control of power systems were prevalent prospects of AI adoption. The major challenging factors to the adoption of AI in the Nigerian energy sector are outdated power system infrastructure, cellular technologies, lack of qualified experts and data science skills, and growing threat from cyber-attacks. The study recommends improved awareness and technical know-how of energy sector personnel, and provision of adequate power system infrastructure to provide stable power supply. https://journals.uj.ac.za/index.php/DigitalFoodEnergy_WaterSystems/article/view/718Artificial intelligenceEnergy SectorFacilities managementMachine Learning |
| spellingShingle | Jonathan Oluwapelumi Mobayo Ayooluwa Femi Aribisala Saheed Olanrewaju Yusuf Usman Belgore The Awareness and Adoption of Artificial Intelligence for Effective Facilities Management in the Energy Sector Journal of Digital Food, Energy & Water Systems Artificial intelligence Energy Sector Facilities management Machine Learning |
| title | The Awareness and Adoption of Artificial Intelligence for Effective Facilities Management in the Energy Sector |
| title_full | The Awareness and Adoption of Artificial Intelligence for Effective Facilities Management in the Energy Sector |
| title_fullStr | The Awareness and Adoption of Artificial Intelligence for Effective Facilities Management in the Energy Sector |
| title_full_unstemmed | The Awareness and Adoption of Artificial Intelligence for Effective Facilities Management in the Energy Sector |
| title_short | The Awareness and Adoption of Artificial Intelligence for Effective Facilities Management in the Energy Sector |
| title_sort | awareness and adoption of artificial intelligence for effective facilities management in the energy sector |
| topic | Artificial intelligence Energy Sector Facilities management Machine Learning |
| url | https://journals.uj.ac.za/index.php/DigitalFoodEnergy_WaterSystems/article/view/718 |
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