Multivariable neural network to postprocess short‐term, hub‐height wind forecasts

Abstract

AbstractThis work introduces a novel error correction method for short‐term, hub‐height wind speed forecasting systems aimed at power output prediction. We present a multivariable neural network that is trained to reduce the error in wind speed predictions out of a numerical weather prediction (NWP) model, by exploiting hidden information in additional atmospheric variables, that is, wind direction, temperature, and pressure. The unique layout of the network was influenced by that of denoising autoencoders, and their ability to learn mapping functions. The predicted values from the NWP model, which incorporate errors due to numerical discretization, inaccuracies in initial/boundary conditions and parametrizations, complex terrain features, etc., are mapped to a more accurate prediction in which the errors have been reduced. To show the performance of the proposed model, training and validation are carried out with 4 years of forecasted and observed data for fifteen sites in a wind farm in Awaji Island, Japan, in a challenging zone with complex topography and therefore complicated, highly fluctuating wind patterns. Moreover, a single variable (i.e., wind speed) network is also implemented in order to expose the contribution and usefulness of including additional atmospheric variables. The results show a considerable reduction in the root mean square error as well as an increase in the correlation coefficient. As expected, it is found that multiple meteorological variables as inputs offer a huge advantage when compared with the equivalent single‐variable correction method.