The electric energy production would be much more efficient if accurate estimations of the future demand were available, since these would allow allocating only the resources needed for the production of the right amount of energy required. With this motivation in mind, we propose a strategy, based on neuroevolution, that can be used to this aim. Our proposal uses a genetic algorithm in order to find a sub-optimal set of hyper-parameters for configuring a deep neural network, which can then be used for obtaining the forecasting. Such a strategy is justified by the observation that the performances achieved by deep neural networks are strongly dependent on the right setting of the hyper-parameters, and genetic algorithms have shown excellent search capabilities in huge search spaces. Moreover, we base our proposal on a distributed computing platform, which allows its use on a large time-series. In order to assess the performances of our approach, we have applied it to a large dataset, related to the electric energy consumption registered in Spain over almost 10 years. Experimental results confirm the validity of our proposal since it outperforms all other forecasting techniques to which it has been compared.

中文翻译：

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深度学习和神经进化的混合：在西班牙短期电能消耗预测中的应用

如果可以获得对未来需求的准确估计，则电能生产将更加高效，因为这些估计将仅分配生产适当数量的所需能源所需的资源。考虑到这种动机，我们提出了一种基于神经进化的策略，可以用于该目标。我们的建议使用遗传算法，以便找到用于配置深度神经网络的次优子集，然后将其用于获取预测。观察到这样的策略是合理的，即通过深度神经网络实现的性能强烈依赖于超参数的正确设置，并且遗传算法在巨大的搜索空间中显示出出色的搜索能力。此外，我们将我们的建议基于分布式计算平台，该平台允许将其用于较大的时间序列。为了评估我们方法的性能，我们将其应用到了一个大型数据集，该数据集与西班牙近十年来注册的电能消耗有关。实验结果证实了我们建议的有效性，因为它优于已与之比较的所有其他预测技术。