Forecasting Wind Power Quantiles Using Conditional Kernel Estimation

The efficient management of wind farms and electricity systems benefit greatly from accurate wind power quantile forecasts. For example, when a wind power producer offers power to the market for a future period, the optimal bid is a quantile of the wind power density. An approach based on conditional kernel density (CKD) estimation has previously been used to produce wind power density forecasts. The approach is appealing because: it makes no distributional assumption for wind power; it captures the uncertainty in forecasts of wind velocity; it imposes no assumption for the relationship between wind power and wind velocity; and it allows more weight to be put on more recent observations. In this paper, we adapt this approach. As we do not require an estimate of the entire wind power density, our new proposal is to optimise the CKD-based approach specifically towards estimation of the desired quantile, using the quantile regression objective function. Using data from three European wind farms, we obtained encouraging results for this new approach. We also achieved good results with a previously proposed method of constructing a wind power quantile as the sum of a point forecast and a forecast error quantile estimated using quantile regression.