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.