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Wind energy is an attractive renewable sources and its prediction is highly essential for multiple applications. Over the literature, there are several studies have been focused on the related researches of synthetic wind speed data generation. In this research, two reconstruction methods are developed for synthetic wind speed time series generation. The modeling is constructed based on different processes including independent values generation from the known probability distribution function, rearrangement of random values and segmentation. They have been named as Rank-wise and Step-wise reconstruction methods. The proposed methods are explained with the help of a standard time series and the examination on wind speed time series collected from Galicia, the autonomous region in the northwest of Spain. Results evidenced the potential of the developed models over the state-of-the-art synthetic time series generation methods and demonstrated a successful validation using the means of mean and median wind speed values, autocorrelations, probability distribution parameters with their corresponding histograms and confusion matrix. Pros and cons of both methods are discussed comprehensively.

A Comparison Between Reconstruction Methods for Generation of Synthetic Time Series Applied to Wind Speed Simulation