Regional Estimation of Wind Power Production in Finland Using k ‐Means Clustering and Power Curve Fitting
Abstract
Wind power has become one of the fastest growing renewable energy sources globally and plays a central role in Finland's electrical grid system, where it already accounts for approximately one quarter of total electricity generation. The increasing penetration of converter‐interfaced wind power plants introduces challenges for grid stability due to reduced system inertia and spatially varying wind conditions. Despite the availability of national wind power production data, detailed regional wind power generation in Finland is not publicly visible, limiting the assessment of regional contributions to power generation and grid support. The paper proposes a regional clustering‐based approach for estimating wind power production using openly available meteorological data and regional wind farm‐specific information. Cluster‐specific power curves are modeled using a four‐parameter logistic function and polynomial regression methods, enabling estimation of regional and overall wind power generation. The proposed framework provides insight into regional power contributions and can be utilized in grid stability studies and transmission planning under increasing wind power penetration.