| Abstract [eng] |
The FMTP described in brief: This thesis gives a detailed study of the impact of Artificial Intelligence (AI) on the large scale sustainable energy project management. The research uses a qualitative multiple case-study method to get insights into the different energy combinations like offshore wind, solar-plus-storage, hybrid renewable systems and smart grids. AI is explored as a tool in minimum and maximum forecasting, maintenance, energy dispatch, and grid reliability, and at the same time, the human, organisational, and regulatory issues that impact its acceptance are scrutinised. The results reveal that AI can enhance the operational performance but moreover its power relies on project context, data readiness, user trust, and institutional conditions. Problem, objective and tasks of the FMTP: 3 Even though the energy sector is turning more to Artificial Intelligence (AI), still very few studies are open on how AI is influencing the management of huge sustainable energy projects in various contexts, especially those involving human, organizational, and regulatory factors. This lack of research makes it difficult to evaluate the role of AI in the integration of complex energy projects. The core aim of this FMTP is to get insights into AI's role in the different stages of large-scale sustainable energy projects, i.e., planning, execution, and monitoring. Research methods used in the FMTP: The research employs qualitative research design grounded in the multiple case-study method. Data were collected from the key participants involved in large sustainable energy projects through semistructured interviewing, and document analysis then supplemented these. Thematic analysis was applied to the data obtained to show AI's role in project management and to compare and contrast the cases with respect to this role. Research and results obtained: The results indicate that AI contributes to the large-scale sustainable energy projects' management by improved forecasting, predictive maintenance, energy dispatch, and reliability. In all the cases, the use of AI resulted in improved operational performance, but the magnitude and type of benefits differed between project categories. Moreover, the findings indicate that interpersonal aspects such as trust, explainability, and training together with data quality, system integration, and regulatory conditions are among the most important factors that determine the success of AI deployment. Conclusions of the FMTP: The investigation arrived at the decision that AI quite possibly turns out to be a great ally in the management of renewable energy projects on a large scale. On the other hand, the effectiveness of the new technology would be determined by the particular situation of the project, the quality of data, the readiness of people, and the state of regulations. AI offers a boost to operational performance in all the ways through which energy is generated, however, its adoption will be success only if there are human-centric system design, proper infrastructure, and the right policies. The findings in this study clearly show that in order to attain the sustainability of project outcomes it is necessary to make AI solutions fit both the technical as well as the organizational requirements. 4 Information about the publication of FMTP results or adaptation for publication The Thesis outcomes have not been released so far. But, these results can easily be converted into a journal article or a conference paper that targets the application of AI in the management of sustainable energy projects. Additionally, some results might be shared with professionals or policymakers by means of publications suitable for the latter, thus keeping practitioners and stakeholders from the energy sector in the loop. |
