Electricity demand is shown to steadily increase in the last few years, and it is one of the key aspects of living standards and quantifying welfare effects. However, the irregularity of electricity demand is one of the main problems in this field. Therefore, it is important to accurately anticipate future expenditures in order to optimize energy generation and to avoid unexpected wastes. As a result, we developed Machine Learning models to predict electricity demand. In particular, our study has been performed using data of the Spanish Electricity Network from 2007 to 2019. To this end, we propose the implementation of a set of Machine Learning techniques using various frameworks. In particular, we implemented six different prediction models: Linear Regression, Regression Trees, Gradient Boosting Regression, Random Forests, Multi-layer Perceptron, and three types of recurrent neural networks. Our experimentation shows promising results in all cases, since our models provides better prediction than the one estimated by the Spanish Electricity Network with an improvement of 12% in the worst case and up to 37% for the best predictor, which turned out to be the Gated Recurrent Unit neural network.

在过去的几年中，电力需求显示出稳定增长，这是生活水平和量化福利影响的关键方面之一。然而，电力需求的不规则性是该领域中的主要问题之一。因此，重要的是准确地预测未来的支出，以优化能源的产生并避免意外的浪费。因此，我们开发了机器学习模型来预测电力需求。特别是，我们的研究是使用2007年至2019年西班牙电力网络的数据进行的。为此，我们建议使用各种框架实施一组机器学习技术。特别是，我们实施了六种不同的预测模型：线性回归，回归树，梯度提升回归，随机森林，多层感知器，以及三种类型的递归神经网络。我们的实验在所有情况下均显示出令人鼓舞的结果，因为我们的模型提供的预测比西班牙电力网络估计的模型更好，在最坏的情况下模型提高了12％，对于最佳的预测器则提高了37％，这证明了门控递归单元神经网络。