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Deep Learning and Time Series Forecasting for Smarter Energy


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.


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