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This study presents comprehensive stakeholder analysis results addressing forecasting challenges for weather-driven energy systems in complex mountainous terrain. Through mixed-methods engagement with 120 practitioners from 50+ organizations across universities, energy providers, and transmission system operators, we quantify implementation priorities and barriers in renewable energy forecasting. Results reveal persistent implementation gaps between stakeholder recognition of forecasting needs and operational deployment. While day-ahead forecasting remains dominant (56% of respondents), extreme weather event management emerges as the critical priority (85% rating), reflecting climate change impacts in mountainous regions. Artificial intelligence integration receives high importance ratings (88%, equivalent to 4.35/5, ± 0.85 SD) but shows limited operational deployment (35%), primarily due to transparency and validation concerns. Mountainous terrain-specific meteorological phenomena – including foehn winds, valley circulation systems, and orographic precipitation effects – are identified as forecasting blind spots requiring specialized modelling approaches and physics-informed machine learning integration. Cross-border coordination challenges receive overwhelming stakeholder attention (96% support for standardized protocols), reflecting the reality that atmospheric phenomena and energy flows regularly cross national boundaries in mountainous regions. Based on quantitative priority assessment and qualitative barrier analysis, we develop an evidence-based implementation roadmap addressing immediate needs (standardized warning systems, validation metrics), medium-term development (AI frameworks, cross-border protocols), and long-term transformation (probabilistic integration, automated decision support). The methodology demonstrates effective approaches for bridging research-practice gaps in energy meteorology applications, with findings contributing to understanding of renewable energy forecasting requirements in complex terrain worldwide. • While 85% of energy stakeholders prioritize extreme weather forecasting in Alpine regions, only 35% have deployed AI solutions. • Significant gap exists between the proportion of stakeholders rating AI as important (85%) and operational deployment (35%). • Cross-border coordination receives 96% support for standardized protocols in complex terrain. • Evidence-based roadmap addresses implementation barriers through coordinated technical and institutional changes. • Mixed-methods stakeholder engagement demonstrates effective approach for bridging research-practice gaps.