The Awareness and Adoption of Artificial Intelligence for Effective Facilities Management in the Energy Sector

Main Article Content

Jonathan Oluwapelumi Mobayo
Ayooluwa Femi Aribisala
Saheed Olanrewaju Yusuf
Usman Belgore

Keywords

Artificial intelligence, Energy Sector, Facilities management, Machine Learning

Abstract

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 The major challenging factors to the adoption of AI in the Nigerian energy sector are outdated power system infrastructure, cellular technologies, lack of qualified experts and data science skills, and growing threat from cyber-attacks.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.

Abstract 232 | PDF Downloads 140

References

[1] R. N. Robinson, Artificial Intelligence : Its Importance, Challenges and Applications in Ni-geria. Direct Resources Journal Engineering Information Technology. 5(5), (2018) 36–41.
[2] A. Babuta, M. Oswald, & A. Janjeva, Artificial Intelligence and UK National Security. Royal United Services Institute Occasional Paper, April 2020. (2020). ISSN 2397-0286.
[3] A. J. Falode, B. O. Faseke, & C. Ikeanyichukwu, Artificial Intelligence: The Missing Critical Component in Nigeria's Security Architecture. (2021) Available at SSRN 3896657.
[4] S. K. Jha, J. Bilalovic, A. Jha, N. Patel, & H. Zhang, Renewable energy: Present research and future scope of Artificial Intelligence. Renewable and Sustainable Energy Reviews, 77, (2017) 297-317.
[5] H. Yousuf, A. Y. Zainal, M. Alshurideh, & S. A. Salloum, Artificial intelligence models in power system analysis. Studies in Computational Intelligence, 912, (2021) 231–242. https://doi.org/10.1007/978-3-030-51920-9_12
[6] A. Sozontov, M. Ivanova, & A. Gibadullin, Implementation of artificial intelligence in the electric power industry. E3S Web of Conferences, 114(01009), 1–6. (2019) https://doi.org/10.1051/e3sconf/201911401009
[7] ConsultancyUK. Artificial Intelligence set to revolutionise energy & utilities industry. Avail-able online (https://www.consultancy.uk/news/16767/artificial-intelligence-set-to-revolu-tionise-energy-utilities-industry). (2018). Accessed on 8/11/2021
[8] S. Küfeoğlu, and M. Özkuran, Energy Consumption of Bitcoin Mining. Cambridge Working Papers in Economics, (2019) 1948.
[9] A. H. Sodhro, S. Pirbhulal, V. H. C. De Albuquerque, Artificial intelligence-driven mecha-nism for edge computing-based industrial applications. IEEE Trans. Ind. Informatics 15, (2019) 4235–4243. doi:10.1109/TII.2019.2902878.
[10] K. W. Kow, Y. W. Wong, R. K. Rajkumar, & R. K. Rajkumar, A review on performance of artificial intelligence and conventional method in mitigating PV grid-tied related power qual-ity events. Renewable and Sustainable Energy Reviews, 56, (2016) 334-346.
[11] A. Youssef, M. El-Telbany, & A. Zekry, The role of artificial intelligence in photo-voltaic systems design and control: A review. Renewable and Sustainable Energy Reviews, 78, (2017) 72-79.
[12] M. Seyedmahmoudian, R. Rahmani, S. Mekhilef, A. M. T. Oo, A. Stojcevski, T. K. Soon, & A. S. Ghandhari, Simulation and hardware implementation of new maximum power point tracking technique for partially shaded PV system using hybrid DEPSO method. IEEE trans-actions on sustainable energy, 6(3), (2015) 850-862.
[13] B. Yang, T. Yu, X. Zhang, H. Li, H. Shu, Y. Sang, & L. Jiang, Dynamic leader based col-lective intelligence for maximum power point tracking of PV systems affected by partial shading condition. Energy Conversion and Management, 179, (2019) 286-303.
[14] T. Ahmad, D. Zhang, C. Huang, H. Zhang, N. Dai, Y. Song, & H. Chen, (2021). Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. Journal of Cleaner Production, Volume 289, 125834, ISSN 0959-6526.
[15] B. Makala, & T. Bakovic, Artificial Intelligence in the Power Sector. Available online: https://www.researchgate.net/publication/343624277. (2020) Assessed on 13/11/2021.
[16] Q. Li, Z. Y. Wu, & A. Rahman, Evolutionary deep learning with extended Kalman filter for effective prediction modeling and efficient data assimilation. Journal of Computing in Civil Engineering, 33(3), (2019) 04019014.
[17] H. Demolli, A. S. Dokuz, A. Ecemis, & M. Gokcek, Wind power forecasting based on daily wind speed data using machine learning algorithms. Energy Conversion and Manage-ment, 198, (2019) 111823.
[18] J. Hou, K. Ni, & A. Hawari, An Artificial Neural Network Based Anomaly Detection Algo-rithm for Nuclear Power Plants. Transactions, 120(1), (2019) 219-222.
[19] P. Neupane, H. Kim, A Conceptual Framework of Facility Management with Intelligence for Sustainable Smart City. Master Thesi: University of Seoul (2020).
[20] I. A. Ajah, & C. C. Chigozie-Okwum, Prospects of ICT for digital growth and national development in Nigeria. African Research Review, 13(3), (2019) 192-203.
[21] A. A. Tived, Artificial Intelligence in the Solar PV value chain : current applications and future prospects future prospects. Masters of Science thesis: KTH Industrial Engineering and Management (2020).
[22] H. Soonmin, A. Lomi, E. C. Okoroigwe, & L. R. Urrego, Investigation of solar energy: The case study in Malaysia, Indonesia, Colombia and Nigeria. International Journal of Renewable Energy Research, 9(1), (2019) 86–95.
[23] T. Dhanabalan, T. A. Sathish, Transforming Indian industries through artificial intelligence and robotics in industry 4.0. International Journal of Mechanical Engineering and Technol-ogy, 9(10), (2018) 835–845
[24] C. R. Kothari Research Methodology – methods and techniques. Second Revised Edition. New Age International (P) Limited, Publishers 4835/24, Ansari Road, Daryaganj, New Delhi – 110002 (2004).
[25] G. Devault, Advantages and Disadvantages of Quantitative Research. Available online: http://www.thebalancesmb.com. (2020) Assessed on 13/11/2021.
[26] A. A. Olanrewaju, J. P Anahve, Duties and responsibilities of quantity surveyors in the Procurement of Building Services Engineering: December, 2015, Procedia Engineering 123: (2015) 352-360, DOI: 10.1016/j.proeng.2015.10.046).
[27] C. C. Adindu, U. O. Ajator, N. N. Agu, V. N. Okorie, & S. O. Yusuf, Enriching Quantity Surveying Curriculum for Leadership in the Built Environment. Proceedings of the 5th Re-search Conference of the NIQS (RECON 5), (2020) 315-331
[28] K. Wakunuma, T. Jiya, & S. Aliyu, Socio-ethical implications of using AI in accelerating SDG3 in Least Developed Countries. Journal of Responsible Technology, 4, (2020) 100006.
[29] N. Kshetri, Artificial intelligence in developing countries. IEEE Annals of the History of Computing, 22(04), (2020) 63-68.