Probabilistic load forecasting with Reservoir Computing
Michele Guerra Simone Scardapane Filippo Maria Bianchi
Some applications of deep learning require not only to provide accurate
results but also to quantify the amount of confidence in their prediction. The
management of an electric power grid is one of these cases: to avoid risky
scenarios, decision-makers need both precise and reliable forecasts of, for
example, power loads. For this reason, point forecasts are not enough hence it
is necessary to adopt methods that provide an uncertainty quantification.
  This work focuses on reservoir computing as the core time series forecasting
method, due to its computational efficiency and effectiveness in predicting
time series. While the RC literature mostly focused on point forecasting, this
work explores the compatibility of some popular uncertainty quantification
methods with the reservoir setting. Both Bayesian and deterministic approaches
to uncertainty assessment are evaluated and compared in terms of their
prediction accuracy, computational resource efficiency and reliability of the
estimated uncertainty, based on a set of carefully chosen performance metrics.