  The intraday (ID) electricity market has received an increasing attention in
the recent EU electricity-market discussions. This is partly because the
uncertainty in the underlying power system is growing and the ID market
provides an adjustment platform to deal with such uncertainties. Hence, market
participants need a proper ID market price model to optimally adjust their
positions by trading in the market. Inadequate historical data for ID market
price makes it more challenging to model. This paper proposes long short-term
memory, deep convolutional generative adversarial networks, and No-U-Turn
sampler algorithms to model ID market prices. Our proposed econometric ID
market price models are applied to the Nordic ID price data and their promising
performance are illustrated.
