Description
Balancing the supply and demand of electrical energy relies heavily on accurate forecasting and probabilistic decision-making. In this talk, we will aim to demystify time series forecasting, and demonstrate how a single forecasting framework built on pandas, scikit and tensorflow allows us to extend simple models by applying transfer learning, auto-encoders and stochastic modelling.