| 31 Mar 2026
A Reproducible Research Framework for Wind Energy
Abstract. With the rapid growth of wind energy installations and the corresponding increase in data-driven research outputs, establishing robust reproducible research (RR) practices has become essential to ensure reliability, transparency, and long-term scientific value. Wind energy research is characterised by high data volumes, complex computational workflows, and increasing reliance on advanced modelling and machine learning techniques, all of which amplify reproducibility challenges. This paper examines the current state of reproducible research practices within the wind energy domain and identifies key gaps that limit computational reproducibility and replicability. In response, it proposes a structured, sector-specific reproducible research workflow designed to improve transparency, reliability, and ease of replication. The proposed workflow spans three stages; Conceptualisation and Planning, Implementation and Execution, and Dissemination. These emphasise essential components such as systematic data management, code sharing, platform selection, version control, and comprehensive documentation. In addition, the paper introduces a set of Python-based tools and best practices that support reproducibility at each stage of the workflow. A practical case study in wind power forecasting using an open-access dataset and a publicly available GitHub repository is presented to demonstrate the application of the workflow and to highlight common reproducibility challenges, particularly those related to data sharing, preprocessing, and documentation. The results show that adopting structured reproducible research practices enables transparent verification, facilitates independent replication, and enhances the reusability of computational wind energy studies. Collectively, the proposed framework and case study provide actionable guidance to support more reliable, verifiable, and collaborative wind energy research.
This paper emphasizes the importance of adopting a reproducible research framework in wind energy research and proposes recommendations for what such a framework should entail. While the underlying motivation is both timely and important, the proposed framework is presented in a way that limits its practical applicability for many researchers in the wind energy community. In the following, I outline several key shortcomings that, in my view, prevent the paper’s recommendations from being readily actionable.
The paper basically describes a software process. Granted, many wind energy related evaluations or computations require resource intense calculations or modeling. For those, such a process as described in this paper is justified. There are, however, many other research avenues in wind energy: observational analyses, the challenge of how to match observations to model output, how to perform wind resource assessments, choosing thresholds for various analyses, dealing with observations, how to use AI in wind energy research, etc. Section 4.2.1 is getting close to this and what I would expect this whole paper to be about. I would argue this paper is rather narrow in scope and more about “A reproducible research framework for complex computational simulations: Applications to wind energy forecasting”. I would further argue that you could substitute wind energy in this paper for any other discipline. What would make the paper wind energy specific? Proposing a reproducible framework to tackle the research avenues I mentioned above would make the paper wind energy specific.
It is not clear to me how a researcher in the wind energy industry would apply what is proposed. The authors state that the framework can be “readily adopted”, but probably only if you have a software engineering background. The framework is very specific to highly computational workflows, and while wind energy does contain them, the research done by the community also contains other workflows. I can see this framework adopted by academia rather than industry. What it lacking is a plan for adoption, a plan for how to convince others to put in the extra effort to follow this approach. Researchers are sometimes hesitant to learn a new coding language or change the way they have been working, so I think equally important to suggesting a framework is to suggest how to adopt a rigorous software process across an entire industry.
An important question that needs to be tackled is also: How does AI play into this? A lot of what you mention in section 3.2 can be done by an AI agent.
The authors state that “The results show that adopting structured reproducible research practices enables transparent verification, facilitates independent replication, and enhances the reusability of computational wind energy studies. Collectively, the proposed framework and case study provide actionable guidance to support more reliable, verifiable, and collaborative wind energy research.” In my opinion, the guidance is not “actionable” without describing how to convince the (international?) wind energy community to adopt such a framework.
Minor comments:
Reference are wrongly formatted throughout the paper.
Line 84: “is presented”
Line 205: “Furthermore”
I’d encourage you to think more about the framing of the paper and what you would like to accomplish. What would make this idea being adopted, adopted in what way? You might want to include more case studies to show how relevant this is across the wind energy community, or narrow the scope of the paper.