Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning

WindEurope Summit 2016

Veronesi, F.^1 ; Grassi, S.^1

Institute of Cartography and Geoinformation, ETH Zurich Stefano, Franscini-Platz 5, Zurich

8093, Switzerland^1

关键词: Confidence interval;    Electricity generation;    Electricity production;    Machine learning models;    Sources of uncertainty;    Statistical methodologies;    Wind resource assessment;    Wind speed time series;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/749/1/012001/pdf
DOI  :  10.1088/1742-6596/749/1/012001

来源: IOP

【 摘 要 】

Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners.