The random and intermittent properties of renewable

The gap

Evidence profile

Sourced from the stated challenges and future work of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 3 journals. Those papers have been cited 51 times in total.

Supporting evidence — 4 representative gaps

• AI-Driven Forecasting of Sustainable Energy Transitions Using Multi-Factor Green Index Modelling (2026) · International Journal of Mathematics And Computer Research · doi

  The complexity of sustainable energy transitions, which depend on several factors. The need for smarter and more flexible tools to predict how these factors will shape the future of energy. The requirement for a meaningful, data-driven, and adaptive AI approach to guide governments and industries in achieving carbon-neutral and energy-secure futures.

  generalstated challenges
  Keywords: complexity sustainable energy transitions depend several factors need
• AI-Driven Forecasting of Sustainable Energy Transitions Using Multi-Factor Green Index Modelling (2026) · International Journal of Mathematics And Computer Research · doi

  1. Integration of Satellite and IoT Data: Including real-time data on emissions, temperature, and renewable output to enhance model accuracy. 2. Explainable AI (XAI): Implementing interpretable models to better understand how each sustainability factor influences the IGTI. 3. Country-Specific Policy Forecasting: Customizing the model for national-level forecasting with localized socio-economic parameters. 4. Hybrid Deep Learning Models: Combining LSTM with attention mechanisms or graph neural networks for improved temporal and spatial insights. 5. Sustainability Simulation Dashboard: Developing an interactive platform where policymakers can visualize and test the effects of various energy scenarios. 14. APPLICATIONS ● Energy Planning Agencies: Accurate forecasts help in designing future renewable energy policies and transition plans. ● Power and Utility Companies: Predictive insights support better grid management and integration of clean energy sources. ● Environmental Organizations: Forecasting helps track progress toward carbon-neutral and climate-resilient goals. ● Government and Policy Makers: The Green Index supports evidence-based decisions for sustainable energy strategies. ● Research and Academic Institutions: The model provides a reliable tool for studying long-term energy trends and sustainability performance. 15. SCOPE ● The model can be used to study and predict future renewable energy trends up to the year 2035. ● It helps identify the major factors that influence a country’s shift toward clean and sustainable energy. ● The research framework can be applied to different countries, regions, or global datasets. ● The Green Energy Index can be expanded by adding new indicators like energy storage, EV adoption, or smart grid data. ● The AI models used in the study can be improved with more real-time data for even more accurate predictions. the research AUTHOR CONTRIBUTIONS ● Bharati Patil conceptualized idea, developed the AI-based forecasting framework, designed the multi-factor Green Energy Index model, and contributed to the interpretation of the experimental findings. ● Komal Korade carried out an extensive literature review, refined the problem statement, and assisted in structuring the overall research methodology. ● Deepashree Mehendale performed data pre-processing, model training, result analysis, and supported the evaluation of the experimental results. ● All authors jointly contributed to writing, editing, and proofreading the manuscript. They have reviewed and approved the final version of the paper for publication.

  generalfuture workevidence 5/5
  Keywords: energy model forecasting renewable models sustainability green index integration real time better factor country policy
• Deep regression analysis for enhanced thermal control in photovoltaic energy systems (2024) · Scientific Reports · cited 51× · doi

  • Expanding the dataset: Collecting a larger and more diverse dataset of thermal images from various PV sys- tems under different conditions will significantly improve the model’s generalization ability and accuracy. • Developing more sophisticated models: Exploring more advanced deep learning architectures, such as recur- rent neural networks or attention mechanisms, to further improve the accuracy and efficiency of the cooling efficiency estimation. • Integrating with other monitoring systems: Integrating the proposed system with other monitoring systems, such as those that track weather conditions, panel performance, and grid connectivity, to provide a more comprehensive view of the PV system’s overall health. • Exploring applications beyond cooling efficiency: Investigating the application of deep learning techniques for other aspects of PV system performance, such as predicting energy output, identifying potential faults, and optimizing energy harvesting. Scientific Reports | (2024) 14:30600 | https://doi.org/10.1038/s41598-024-81101-x 18 www.nature.com/scientificreports/ • Developing a user-friendly interface: Developing a user-friendly interface that allows operators to easily ac- cess and interpret the data generated by the proposed system. This research provides a strong foundation for future advancements in the field of photovoltaic thermal management optimization. By addressing the limitations and pursuing the proposed future work, this research can contribute significantly to the development of more efficient and sustainable PV systems.

  generalfuture workevidence 5/5
  Keywords: system developing efficiency systems proposed dataset thermal conditions improve accuracy exploring deep learning cooling integrating
• Deep learning-based time-series solar power prediction for automatic multisource energy generation systems (2026) · Frontiers in Mechanical Engineering · doi

  The random and intermittent properties of renewable sources impose a great challenge in the operation of the systems and energy management. The limited availability of data for training and testing deep learning models. The need for a more comprehensive system that incorporates multiple sources of energy.

  generalstated challengesevidence 5/5
  Keywords: random intermittent properties renewable sources impose great challenge

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