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Neural networks are increasingly being used in a variety of settings topredict wind direction and speed, two of the most important factors forestimating the potential power output of a wind farm. However, thesepredictions are arguably of limited value because classical neural networkslack the ability to express uncertainty. Here we instead consider the use ofBayesian Neural Networks (BNNs), for which the weights, biases and outputs aredistributions rather than deterministic point values. This allows for theevaluation of both epistemic and aleatoric uncertainty and leads towell-calibrated uncertainty predictions of both wind speed and power. Here weconsider the application of BNNs to the problem of offshore wind resourceprediction for renewable energy applications. For our dataset, we useobservations recorded at the FINO1 research platform in the North Sea and ourpredictors are ocean data such as water temperature and current direction.  The probabilistic forecast predicted by the BNN adds considerable value tothe results and, in particular, informs the user of the network's ability tomake predictions of out-of-sample datapoints. We use this property of BNNs toconclude that the accuracy and uncertainty of the wind speed and directionpredictions made by our network are unaffected by the construction of thenearby Alpha Ventus wind farm. Hence, at this site, networks trained onpre-farm ocean data can be used to accurately predict wind field informationfrom ocean data after the wind farm has been constructed.

Bayesian neural networks for the probabilistic forecasting of wind direction and speed using ocean data