The rise and implications of artificial intelligence (AI): An energy sector management perspective

Submission deadline date: 31 July 2024


The rise of artificial intelligence (AI) has changed the field of energy sector management by providing players with a new technology to power economic development in society (Banga, 2020; Ahmad et al., 2022). The global AI in energy market is expected to reach 7.78 billion USD by 2024 (Ahmad et al., 2022) and 28.3 Billion USD by 2028 (EmergenResearch, 2022). Rarely the emergence of a new technology has received such a huge interest from energy practitioners, academics and regulators. This may be explained by the dual sentiments that it arouses: a mix of a ‘great hope’ and a ‘big fear’. According to F. Westerheide, the CEO of AI for Humans, “whoever controls the strongest artificial intelligences controls the world.” (Forbes, Nov 27, 2019). The energy sector has been at the forefront of introducing AI. In fact, the CEO of TotalEnergies (2019) pointed out that “The energy sector is an ideal field of study” for the development of AI because of AI’s “huge potentials” for improving industrial efficiency, energy performance and rates forecasting. In this perspective, TotalEnergies launched a partnership with Google Cloud’s machine-learning and Tata Consultancy Services to cultivate and reap the benefits of AI. Indeed, AI can help in addressing important challenges on both the demand-side and the supply-side of value chain activities (Li et al., 2023). On the demand-side, AI methods can help for example in demand forecasting (Bedi and Toshniwal, 2019), dynamic pricing, and scheduling and controlling devices (Antonopoulos et al., 2020). On the supply-side, AI can enhance operation strategy for distributed energy systems (Li et al., 2022), forecast short-term energy loads to enable reliable and evidence based scheduling of energy production processes (Hu et al., 2022), and provide renewable and affordable electricity from complex sources in a secure manner (Ahmad et al., 2022). Nevertheless, the adoption of AI may result in substantial challenges such as jeopardizing existing jobs (Frey et al., 2017) , creating demand for qualified experts and data science skills, and changing technical infrastructure. It may also increase risks in terms of data security and loss of control (Joran, 2019), and compliance and legal security risks (Ahmad et al, 2021 and 2022).

Aims of the Special Issue

The objective of this special issue on “The rise and implications of artificial intelligence (AI): An energy sector management perspective” is to create knowledge about the functioning and effects of AI and how to unlock its tremendous potentials for the energy sector management. AI is defined as “the ability of machines to perform human-like cognitive tasks, including the automation of physical processes such as manipulating, moving objects, sensing, perceiving, problem solving, decision making and innovation” (Benbya, Davenport and Pachidi, 2020, p.  9). Examples of AI deployed in the energy sector include (i) fuzzy logic systems, (ii) artificial neural networks, (iii) genetic algorithm, and (iv) expert system techniques (Ahmad et al., 2022). 

The special issue aims to stimulate debates towards the development of conceptual frameworks and strategies aimed to improving ‘organization-AI’ interactions and ‘human-AI’ interactions. The special issue seeks to address three core areas: 

  1. Applications and operationalizations of AI in the energy field: 
    • What are the best practices in using AI across the value chain stages (down-stream and up-stream) and types of energies (renewable and non-renewable)?
    • To what extent are companies aware of and involved in operationalizing the advances of AI for energy efficiency?
    • What are the challenges and benefits of implementing AI in the energy field? 
  2. The effects of the advent of AI on energy sustainability approaches and strategies: 
    • How will AI improve the sustainability of energy production, distribution, consumption, and conservation?
    • What models of energy sustainability measurement are appropriate for the evaluation of AI systems that the capabilities keep exponentially improving across time and space?
    • How to revisit the current approaches of individual accountability and corporate social responsibility bound to AI emergence, and what are the implications for energy policy makers?
    • What are key ethical questions that need to be asked and answered to support the development and operationalization of AI in the field of energy sector management?
  3. Humans and AI capabilities: Are they complementary or competing? 
    • What new job roles and careers paths are emerging as implications of the emergence of AI in the energy sector?
    • How can energy firms reconcile human capabilities with machine learning capabilities? 
    • How can companies avoid scenarios of conflict and loss of control that are inherent to the adoption of AI?
    • How will AI redefine the work of the managers in the energy sector?

Submissions Information

The guest editors welcome submissions that pursue themes that have a bearing on (potential) developments and uses of AI in the energy sector, the business transformations engendered by the rise of these disruptive technologies, as well as their implications in terms of accountability and ethics. Submissions are encouraged from all theoretical perspectives and methodological approaches, including theory papers with a view to examining the advances of AI, cases of its use in specific settings, and the challenges, risks and benefits of AI in the field of energy. 

A significant theoretical contribution to energy sector management is the main criterion for publication in IJESM. The guest editors seek to publish papers that change, challenge, or fundamentally advance our knowledge of the concepts, relationships, models, or theories embedded in the extant literatures. Relevant theoretical lenses include, among others: technology acceptance theory, neuro-cognitive theories (i.e. cognitive computing, neuro-genetic), resources efficiency approaches, risk management frameworks, innovation diffusion theory, optimization approaches, human-computer interaction theories. 

The special issue is open to a broad range of studies in terms of the level of analysis. We welcome manuscripts that focus on individual, organizational, or institutional levels. Meso-level approaches are also encouraged. The main requirement is that the scope and topicality of the manuscripts should address socio-economic and managerial issues pertaining to the advent and use of AI  (Kolbjørnsrud et al,, 2016; Raisch, S., & Krakowski, S., 2021) for energy production, distribution, consumption and/or conservation.

Submissions are made using ScholarOne Manuscripts. Registration and access are available here.

Author guidelines must be strictly followed. Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to "Please select the issue you are submitting to".

Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.

Click here to submit!

Key Deadlines 

Submissions now open!

Closing date for manuscript submissions: 31 July, 2024


Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. Journal of Cleaner Production, 289, 125834.
Ahmad, T., Zhu, H., Zhang, D., Tariq, R., Bassam, A., Ullah, F., AlGhamdi, A.S. and Alshamrani, S.S., 2022. Energetics systems and artificial intelligence: applications of industry 4.0. Energy Reports, 8, pp.334-361.
Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S. and Wattam, S., 2020. Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review. Renewable and Sustainable Energy Reviews, 130, p.109899. 
Bedi, J., & Toshniwal, D. (2019). Deep learning framework to forecast electricity demand. Applied Energy, 238, 1312-1326.
Benbya, H., Davenport, T. H., & Pachidi, S. (2020). Artificial intelligence in organizations: Current state and future opportunities. MIS Quarterly Executive, 19(4).
EmergenResearch, Artificial Intelligence in Energy Market, Forecast to 2028, Published Date: Feb 2021. Report ID: ER_00518, p. 250, accessed on March 18, 2023,…;
Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation?. Technological forecasting and social change, 114, 254-280.
Hu, Y., Li, J., Hong, M., Ren, J., & Man, Y. (2022). Industrial artificial intelligence based energy management system: Integrated framework for electricity load forecasting and fault prediction. Energy, 244, 123195.
Jordan, M. I. (2019). Artificial intelligence—the revolution hasn’t happened yet. Harvard Data Science Review, 1(1), 1-9.
Kolbjørnsrud, V., Amico, R., & Thomas, R. J. (2016). How artificial intelligence will redefine management. Harvard Business Review, 2(1), 3-10.
Li, J., Ma, S., Qu, Y., & Wang, J. (2023). The impact of artificial intelligence on firms’ energy and resource efficiency: Empirical evidence from China. Resources Policy, 82, 103507.
Li, C., Li, Z., Zhu, H., Tian, Z., & Feng, W. (2022). Study on operation strategy and load forecasting for distributed energy system based on Chinese supply-side power grid reform. Energy and Built Environment, 3(1), 113-127.
Pouyanne, P., 2019, “Artificial Intelligence And The Energy Sector: Huge Potential, Tough Questions”, accessed on March 15, 2023.…
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192-210.