  Electricity supply must be matched with demand at all times. This helps
reduce the chances of issues such as load frequency control and the chances of
electricity blackouts. To gain a better understanding of the load that is
likely to be required over the next 24h, estimations under uncertainty are
needed. This is especially difficult in a decentralized electricity market with
many micro-producers which are not under central control.
  In this paper, we investigate the impact of eleven offline learning and five
online learning algorithms to predict the electricity demand profile over the
next 24h. We achieve this through integration within the long-term agent-based
model, ElecSim. Through the prediction of electricity demand profile over the
next 24h, we can simulate the predictions made for a day-ahead market. Once we
have made these predictions, we sample from the residual distributions and
perturb the electricity market demand using the simulation, ElecSim. This
enables us to understand the impact of errors on the long-term dynamics of a
decentralized electricity market.
  We show we can reduce the mean absolute error by 30% using an online
algorithm when compared to the best offline algorithm, whilst reducing the
required tendered national grid reserve required. This reduction in national
grid reserves leads to savings in costs and emissions. We also show that large
errors in prediction accuracy have a disproportionate error on investments made
over a 17-year time frame, as well as electricity mix.
