Torres J F, Martínez-Álvarez F, Troncoso A
Data Science and Big Data Lab, Universidad Pablo de Olavide, 41013 Seville, Spain.
Neural Comput Appl. 2022;34(13):10533-10545. doi: 10.1007/s00521-021-06773-2. Epub 2022 Feb 5.
Nowadays, electricity is a basic commodity necessary for the well-being of any modern society. Due to the growth in electricity consumption in recent years, mainly in large cities, electricity forecasting is key to the management of an efficient, sustainable and safe smart grid for the consumer. In this work, a deep neural network is proposed to address the electricity consumption forecasting in the short-term, namely, a long short-term memory (LSTM) network due to its ability to deal with sequential data such as time-series data. First, the optimal values for certain hyper-parameters have been obtained by a random search and a metaheuristic, called coronavirus optimization algorithm (CVOA), based on the propagation of the SARS-Cov-2 virus. Then, the optimal LSTM has been applied to predict the electricity demand with 4-h forecast horizon. Results using Spanish electricity data during nine years and half measured with 10-min frequency are presented and discussed. Finally, the performance of the proposed LSTM using random search and the LSTM using CVOA is compared, on the one hand, with that of recently published deep neural networks (such as a deep feed-forward neural network optimized with a grid search) and temporal fusion transformers optimized with a sampling algorithm, and, on the other hand, with traditional machine learning techniques, such as a linear regression, decision trees and tree-based ensemble techniques (gradient-boosted trees and random forest), achieving the smallest prediction error below 1.5%.
如今，电力是任何现代社会福祉所必需的基本商品。由于近年来电力消耗的增长，主要是在大城市，电力预测对于为消费者管理高效、可持续和安全的智能电网至关重要。在这项工作中，提出了一种深度神经网络来解决短期电力消耗预测问题，即长短期记忆（LSTM）网络，因为它能够处理诸如时间序列数据之类的顺序数据。首先，通过随机搜索和一种基于严重急性呼吸综合征冠状病毒2（SARS-Cov-2）病毒传播的元启发式算法——冠状病毒优化算法（CVOA），获得了某些超参数的最优值。然后，将最优的LSTM应用于预测4小时预测范围内的电力需求。给出并讨论了使用西班牙九年半以10分钟频率测量的电力数据的结果。最后，一方面将使用随机搜索的拟议LSTM和使用CVOA的LSTM的性能与最近发表的深度神经网络（如用网格搜索优化的深度前馈神经网络）和用采样算法优化的时态融合变压器的性能进行比较，另一方面与传统机器学习技术（如线性回归、决策树和基于树的集成技术（梯度提升树和随机森林））进行比较，实现了低于1.5%的最小预测误差。
