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In this study, a wind speed analysis is conducted using Reanalysis wind speed data for the height of 50 meters using five probability distributions that were tested and compared using the Maximum Likelihood Method (MLM) for estimating the distributions parameters and three goodness-of-fit tests for selecting the best fitting one, namely the Akaike Information Criterion (AIC), The Bayesian Information Criterion (BIC) and the Anderson-Darling (AD). The wind roses, histograms, wind map and the wind power density maps were established. For complementarity between solar and wind, an assessment based on energy fluctuations is adopted and a new complementarity metric is proposed. Using reanalysis data and satellite-based data, a wind turbine model and a PV systems output data are used. This method uses a combination of normalization and a distance metric. Firstly, the outliers are removed, then the daily power output data for PV and Wind turbine are scaled using the minimum-maximum normalization. This normalization transforms both energies data into the same range of 0-1, where the minimum is equal to 0 and the maximum is equal to 1, while conserving its structure, hence, this allows for comparison between the two sources and identify days with high complementarity, for instance when one source is close to 1 and the other is close to zero. For complementarity assessment, the Euclidean distance is adopted. This distance is calculated for each between the normalized values of both sources, and it is between 0 and 1; higher distance indicates high complementarity level. [ABSTRACT FROM AUTHOR]