Abstract
The predicted wind power in coastal waters is an important factor when planning and developing offshore wind farms. The stochastic wind field challenges the accuracy of these predictions. Using single-point wind measurements, most previous studies have focused on the prediction of short-term wind power, ranging from minutes to several days. Longer-term wind power predictions would better support decision-making related to offshore wind power balance management and reserve capacities. In addition, larger-scale wind power predictions, based on gridded wind field data, would provide a more comprehensive understanding of the spatiotemporal variations of wind energy resources. In this study, a spatiotemporal ordinary kriging model was developed to predict the offshore wind power density on a monthly basis using the cross-calibrated multiplatform gridded wind field data. The spatiotemporal variations of wind power density were directly quantified through the development of spatiotemporal variograms that integrated spatial and temporal distances. The proposed model achieved a notable performance with an overall R2 of 0.94 and a relative prediction error of 16.35% in the validation experiment of predicting the monthly wind power density from 2013 in the coastal waters of China’s Guangdong Province. Using this model, the spatial distributions of wind power density along Guangdong’s coastal waters at monthly, seasonal, and annual time-scales from 2013 were accurately predicted. The experiment results demonstrated the remarkable potential of the spatiotemporal ordinary kriging model to provide reliable long-term prediction for offshore wind energy resources.
Highlights
Development of new renewable clean energy resources, such as wind energy, is one of the most effective ways to reduce excessive traditional energy consumption and control greenhouse gas emissions and global warming
Temporal, and spatiotemporal variograms In January 2003 to December 2012 (120 months), 82,080 grid-based offshore wind power density (WPD) measurements were collected in the coastal waters of Guangdong Province to support calculation of the empirical spatial and temporal variograms and their fitted theoretical models
The relatively low relative prediction error (RPE) level indicated that the WPD prediction errors were reasonable and controllable when compared to the observed WPD values
Summary
Development of new renewable clean energy resources, such as wind energy, is one of the most effective ways to reduce excessive traditional energy consumption and control greenhouse gas emissions and global warming. Offshore areas contain relatively rich and stable wind energy resources compared to onshore areas (Zheng et al, 2012). Offshore wind has enormous potential in many regions, as many countries search for competitive, zero-carbon energy sources that can be deployed relatively quickly and at a large scale (Global Wind Energy Council (GWEC), 2018). The industrialization and large-scale utilization of offshore wind energy resources require a comprehensive understanding of the spatial and temporal characteristics of sea surface wind fields. In response to these challenges, wind power prediction can be used to improve the balance management and capacity planning of wind power generation (Tascikaraoglu and Uzunoglu, 2014)
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