Generating scenarios from probabilistic short-term load forecasts via non-linear Bayesian regression

In this paper we present a simple and intuitive method for fitting a non-linear Bayesian regression model on short-term load forecasts. Such models have been implemented via Bayesian neural networks, which are known for their hyper-parameter sensitivity. We instead show a more general method to fit any regression model and demonstrate this by using a tree-model. Further, we evaluate the results against non-linear quantile regression, a common technique in probabilistic load forecasting. The resulting model allows to generate samples for future scenarios and thus can be applied to operations problems such as dynamic control of battery storage, an application that quantile regression is unfit for.