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.

电力供应必须始终与需求匹配。这有助于减少出现问题的机会，例如负载频率控制和断电的机会。为了更好地了解未来24小时内可能需要的负载，需要进行不确定性下的估算。在具有许多不受中央控制的微型生产者的分散电力市场中，这尤其困难。

在本文中，我们调查了11种离线学习和5种在线学习算法对预测未来24小时用电情况的影响。我们通过在基于代理的长期模型ElecSim中进行集成来实现这一目标。通过预测未来24小时的电力需求情况，我们可以模拟针对日间市场的预测。一旦做出了这些预测，就可以从剩余分布中采样并使用仿真ElecSim扰动电力市场需求。这使我们能够了解错误对电力分散市场的长期动态的影响。

我们证明，与最佳的离线算法相比，使用在线算法可以将平均绝对误差降低30％，同时减少所需的招标国家电网储备。国家电网储备的减少导致成本和排放的节省。我们还表明，在预测准确性方面存在较大误差的情况下，在17年的时间范围内进行的投资以及用电结构的误差不成比例。