  Retailers and major consumers of electricity generally purchase an important
percentage of their estimated electricity needs years ahead in the forward
market. This long-term electricity procurement task consists of determining
when to buy electricity so that the resulting energy cost is minimised, and the
forecast consumption is covered. In this scientific article, the focus is set
on a yearly base load product from the Belgian forward market, named calendar
(CAL), which is tradable up to three years ahead of the delivery period. This
research paper introduces a novel algorithm providing recommendations to either
buy electricity now or wait for a future opportunity based on the history of
CAL prices. This algorithm relies on deep learning forecasting techniques and
on an indicator quantifying the deviation from a perfectly uniform reference
procurement policy. On average, the proposed approach surpasses the benchmark
procurement policies considered and achieves a reduction in costs of 1.65% with
respect to the perfectly uniform reference procurement policy achieving the
mean electricity price. Moreover, in addition to automating the complex
electricity procurement task, this algorithm demonstrates more consistent
results throughout the years. Eventually, the generality of the solution
presented makes it well suited for solving other commodity procurement
problems.
