I would like to commend the authors for their insightful work entitled “Machine Learning for Optimising Renewable Energy and Grid Efficiency.” This paper addresses a highly relevant and timely topic, offering an extensive exploration of machine learning (ML) applications in renewable energy systems and their role in achieving Net Zero emissions. However, I have several questions and comments regarding the study. Please note that my remarks aim to foster constructive dialogue and contribute to advancements in this crucial field:
The manuscript presents a broad overview of ML models and their potential to enhance energy forecasting, grid optimisation, and storage management. While these aspects are well outlined, there are significant concerns regarding the methodological rigor and practical applicability of the findings. The study discusses a variety of ML techniques, such as LSTM, Random Forest, and SVM, but does not provide sufficient empirical validation or real-world case studies to support the claimed improvements in grid and storage efficiency. This omission limits the reliability and transferability of the proposed approaches.
Additionally, the data collection and preparation process, while briefly described, lacks the transparency needed to ensure reproducibility. The sanitisation techniques, selection of training/testing datasets, and the handling of missing or incomplete data are not adequately detailed. Without such clarity, the credibility of the reported results is diminished. Moreover, the paper relies heavily on secondary data sources, with no explicit mention of primary data acquisition, raising questions about the robustness of the findings.
The discussion on the impact of ML on renewable energy systems is insightful but occasionally overgeneralized. For example, the claim that ML could close the “ambition gap” by 20% lacks clear evidence or a robust analytical framework. Similarly, the quantified emission reductions attributed to specific energy sources, such as wind and solar, appear optimistic and are not substantiated by clear calculations or baselines. Greater precision and context in presenting these findings would enhance their credibility and practical significance.
Furthermore, the cited literature, while extensive, exhibits some notable gaps. Critical studies on ML applications in energy systems and relevant case studies appear to be missing, potentially biasing the review and narrowing its scope. There is also limited critical evaluation of the limitations and trade-offs of the ML models discussed, such as their performance under high-dimensional or noisy data conditions typical of energy systems. Such evaluations are essential to provide a balanced perspective and avoid overgeneralized conclusions.
The proposed ML framework for renewable energy optimisation is conceptually compelling, but its feasibility remains speculative. The study does not present simulation results, real-world implementations, or detailed performance metrics to validate its claims. Without empirical evidence, it is challenging to assess the scalability or robustness of the proposed solutions. Additionally, ethical considerations, such as data privacy, cybersecurity, and the potential environmental costs of large-scale ML deployments, are mentioned only briefly and warrant a more thorough exploration.
Lastly, the manuscript could benefit from improved structure and conciseness. Repetitive discussions and verbose sections detract from the overall readability and clarity of the paper. A more streamlined presentation focused on key findings and actionable insights would strengthen its impact and accessibility to the target audience.
I look forward to the authors’ responses to these points and their thoughts on addressing these limitations. I believe this will enhance the paper’s contribution to the field and pave the way for meaningful advancements in the integration of machine learning into renewable energy systems.