Purpose: This paper seeks to determine how AI can be used in the furtherment and management of distributed generation of renewable power, and how it can address challenges arising from the use of distributed generation of renewable energy sources like solar and wind. Thus, this research addresses a gap in the knowledge base related to AI technologies to plan and optimize the performance of decentralized energy systems, based on the assessment of opportunities, risks, and applications of the technology. Methodology: Convenience sampling was used and to incorporate mixed methods, the Research Onion of Saunders was applied to attain methodological credibility. Self-administered questionnaires were distributed to 210 professionals drawn from renewable energy and AI industries. The data was gathered by an online survey instrument and closed was done by employing basic descriptive statistics, chi-square tests, one-way ANOVA, sentiment analysis, clustering method, and multiple linear regression. Fortunately, the analysis also incorporated liberal graphical displays so that there was a better feel of the data distribution and association patterns. Results: The study reveals a generally positive perception of AI’s benefits in managing decentralized energy grids among respondents, who average 13.41 years of experience in renewable energy and 14.59 years in AI applications, though this experience varies significantly (SD = 8.84 and 7.91, respectively). Statistical analyses showed no significant correlation between respondents' familiarity with decentralized grids and their perceptions of AI's role. The Chi-Square Test for Independence (Chi-Square = 21.8, p = 0.15) and ANOVA (F = 1.17, p = 0.324) indicate no significant association between grid familiarity and AI perceptions, nor differences in AI experience across varying levels of grid familiarity. Sentiment analysis also confirmed these patterns when AI was only rated neutrally to slightly positively; the average score was 1. 0 illustrating people’s relative agreement about the potential of AI, even if they may not personally have a lot of experience with it. Primary data analysis by clustering showed no significant dissimilarities of response patterns indicative of the homogeneity of the sample. Furthermore, the result obtained from multiple linear regression (R² = 0. 01, p = 0. 329) indicates that both experience in the renewable and energy and experience in the use of AI have no significant impact towards the perception of the role of AI in distributed grids. Conclusion: AI has a high potential and applicability for decentralized energy grids but to achieve successful implementation certain technical, operational and policy issues need to be addresses. Hence, the study points out the required future research and stakeholder cooperation in order to explore all the possibilities of AI application in this field with the specific suggestions of the directions of future studies and interdisciplinary cooperation. Practical Implications: The study focuses on the shift in the use of artificial intelligence, in the operation of decentralized re-new able energy grids and offers guidance for grid operators, policymakers as well as technology developers. By drawing awareness of the advantage and drawbacks of AI implementation, the experts will be able to solve the issues related to the improvement of grid reliability and the stable integration of renewable sources into the global energy infrastructure. Originality/Value: Following this introduction, this paper aims to present a quantitative and qualitative analysis of decentralized renewable energy grids with consideration of AI with the following organization of the work. By applying Research Onion framework of Saunders, this research enhances the methodological robustness and can be helpful for major stake holders, including academics, business and or policymakers in the domain of AI as well as renewable energy.

This work overviews how machine learning and deep learning have revolutionized the present era of computer science. Artificial intelligence has evolved greatly in modern computer science through machine learning and deep learning. It avails inducements in several fields, such as natural language processing, computer vision, and robotics. It is enhancing the effectiveness of data analysis itself. These technologies also presented new opportunities for creativity. It includes privacy of data, the bias inherent in algorithms, and the limited, or obscure, interpretability of most models. They included the development of algorithms that are more efficient, the issue of interpretability, and the consideration of ethical concerns in the future. It is useful to gain insights on the part that these technologies play in modern computing paradigm approaches It is implemented in business and industries and a glimpse of what these technologies are likely to become in the future. It includes supervised, unsupervised, and reinforcement learning methodologies that enable the model to be used in various investigations ranging from predictive modeling to intelligent task management. The process requires vast data, second, computation power, and third, the expertise of the right algorithm selection. The obscuration of some of the algorithms can make it challenging to explain results as questions about accountability and transparency emerge. It is expected that machine learning and deep learning will witness a significant leap, mostly owing to data availability as well as enhancements in computational capabilities. These technologies, as they become increasingly implemented within organizations, will enable better organizational decision-making while also pushing the boundaries of possibility within the field of computers. The continued advancement of machine learning and deep learning precipitate further development for more applications.