Toward improved siting of wind-solar hybrid farms: A novel framework integrating multi-criteria decision making, machine learning-driven feature selection, and spatial clustering - Uguz, Yasin Ferit; Bilgili, Atakan; Uzar, Melis (ELSEVIER SCI LTD, 2026)
Ensuring secure, affordable, and sustainable energy supply is a growing global challenge as rising demand and supply insecurity accelerate the transition to renewable energy. Hybrid wind-solar systems offer an effective solution by exploiting spatial and temporal resource complementarity, but their deployment critically depends on reliable site selection. Most multi-criteria decision analysis studies rely on a single weighting method, apply limited criteria evaluation, lack statistical validation, and rarely identify spatially coherent, investment-ready zones. This study addresses these gaps by proposing an integrated geographic decision-making framework for hybrid wind-solar siting that combines two expert-based weighting methods, Analytical Hierarchy Process (AHP) and Best-Worst Method (BWM) with an objective, data-driven weighting method, CRiteria Importance Through Intercriteria Correlation (CRITIC) and machine learning-assisted criterion screening. The framework was applied to Izmir Province, T & uuml;rkiye. Suitability maps generated under alternative weighting methods were compared using spatially adjusted statistical tests, clustered into contiguous high-suitability zones using hierarchical density-based spatial clustering (HDBSCAN), converted into candidate-site polygons, and prioritized using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and the Multi-Attributive Border Approximation Area Comparison (MABAC) method. The results revealed pronounced differences across weighting methods. Highly or very highly suitable areas accounted for 33.27% (AHP) and 27.75% (BWM) of candidate sites, compared with 58.39% under CRITIC. Validation using 703 existing solar photovoltaic and wind turbine installations showed agreement rates of 77.81%, 72.83%, and 83.65%, respectively. The resulting outputs provide actionable decision-support information for regional planning and the identification of investment-ready hybrid wind-solar zones.
Keywords: Hybrid energy site selection; Geographic information systems; Multi-criteria decision making; Machine learning; Feature selection; Clustering; ANALYTIC HIERARCHY PROCESS; SITE SELECTION; GIS; MODEL; AHP; PERFORMANCE; IMPACTS; SYSTEM; REGION; Energy & Fuels; Engineering