Time series wind power forecasting based on variant Gaussian process and TLBO

Due to the variability and stochastic nature of wind power system, accurate wind power forecasting has an important role in developing reliable and economic power system operation and control strategies. As wind variability is stochastic, Gaussian Process regression has recently been introduced to capture the randomness of wind energy. However, the disadvantages of Gaussian Process regression include its computation complexity and incapability to adapt to time varying time-series systems. A variant Gaussian Process for time series forecasting is introduced in this study to address these issues. This new method is shown to be capable of reducing computational complexity and increasing prediction accuracy. It is further proved that the forecasting result converges as the number of available data approaches innite. Further, a teaching learning based optimization (TLBO) method is used to train the model and to accelerate
the learning rate. The proposed modelling and optimization method is applied to forecast both the wind power generation of Ireland and that from a single wind farm to show the eectiveness of the proposed method.

An on-line application for ΔR calculation

A regional offset (ΔR) from the marine radiocarbon calibration curve is widely used in calibration software (eg CALIB, OxCal) but often is not calculated correctly. While relatively straightforward for known age samples, such as mollusks from museum collections or banded corals, it is more difficult to calculate ΔR and the uncertainty in ΔR for 14C dates on paired marine and terrestrial samples. Previous researchers have often utilized classical intercept methods (Reimer et al. 2002; Dewar et al. 2012, Russell et al. 2011) but this does not account for the full calibrated probability density function (PDF). We have developed an on-line application for performing these calculations for known age, paired marine and terrestrial 14C dates, or U-Th dated corals which is available at http://calib.qub.ac.uk/deltar