Wind Power Forecasting at Turbine Level: Spatial Correlation and the Value of Neighbouring Sensors
DOI:
https://doi.org/10.5281/zenodo.22660876Keywords:
Wind Power Forecasting at Turbine Level; Applied machine learning; Evidence synthesis; Production deployment; MLOpsAbstract
Wind Power Forecasting at Turbine Level is an area of active development in applied machine learning, with evidence that has
accumulated unevenly across its component questions. We review the current evidence base for wind power forecasting at turbine
level, spanning methodological, implementation, governance, and cost dimensions, focusing on where practice has converged and
where it continues to vary, and set out the practical considerations relevant to a team considering its adoption. We conclude that
selective, context-specific use is best supported by the evidence currently available, rather than uniform adoption or avoidance.
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