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In this study, we analyze observational and predicted wind energy datasets of the lower 48 states of the United States, and we intend to predict an optimal map for new turbines placement. Several approaches have been implemented to investigate the correlation between current wind power stations, power capacity, wind seasonality, and site selection. The correlation between stations is carried out according to Pearson correlation coefficient approach joined with the spherical law of cosines to calculate the distances. The high correlation values between the stations spaced within a distance of 100 km show that installing more turbines close to the current farms would assist the electrical grid. The total power capacity indicates that the current wind turbines are utilizing approximately 70% of the wind resources available in the turbine’s sites. The Power spectrum of Fourier’s spectral density indicates main, secondary, and harmonic frequencies correspond to yearly, semiyearly, and daily wind-speed periodic patterns. We propose and validate a numerical approach based on a novel fuzzy logic framework for wind turbines placement. Map optimizations are fitted considering different parameters presented in wind speed, land use, price, and elevation. Map optimization results show that suitable sites for turbines placement are in general agreement with the direction of the correlation approach.

Map Optimization Fuzzy Logic Framework in Wind Turbine Site Selection with Application to the USA Wind Farms