A methodology for the synthetic generation of hourly wind speed time series based on some known aggregate input data

► We proposed a methodology for synthetic generation of hourly wind speed time series. ► The generation model is able to adapt to a different number and type of input data. ► Model validation is carried out examining two Italian localities. ► Model performances are assessed comparing features of measured and generated data. ► Best results are obtained when mean and maximum measured wind speeds are known. The availability of hourly wind speed data is becoming increasingly important for ensuring the proper design of wind energy conversion systems. For many sites, measured series of such high resolution are incomplete or entirely lacking; hence the need for a model for synthesizing wind speed data. The objective of this paper is to construct a model for synthetically generating hourly wind speed data, adopting a physical–statistical approach. This generation model defines four parameters for characterizing the wind speed time series in terms of probability distribution and autocorrelation functions. As opposed to the numerous methodologies reported in literature, the proposed approach can be adapted to a different number and type of available input data. Model validation has been carried out by examining two Italian sites, having different characteristics in terms of mean monthly wind speeds and autocorrelation function. To demonstrate its flexibility, in both sites wind speed time series have been synthesized for three different cases, increasing the amount of known input data.