  We quantify the value of sub-seasonal forecasts for a real-world prediction
problem: the forecasting of French month-ahead energy demand. Using surface
temperature as a predictor, we construct a trading strategy and assess the
financial value of using meteorological forecasts, based on actual energy
demand and price data. We show that forecasts with lead times greater than 2
weeks can have value for this application, both on their own and in conjunction
with shorter range forecasts, especially during boreal winter. We consider a
cost/loss framework based on this example, and show that while it captures the
performance of the short range forecasts well, it misses the marginal value
present in the longer range forecasts. We also contrast our assessment of
forecast value to that given by traditional skill scores, which we show could
be misleading if used in isolation. We emphasise the importance of basing
assessment of forecast skill on variables actually used by end-users.
