Abstract Electricity load forecasting has been a substantial problem in the electric power system management process. An accurate forecasting model is essential to avoid imprecise predictions that can negatively affect system efficiency, economy, and sustainability. Among several prediction techniques, deep learning methods, especially the Long Short-Term Memory (LSTM), have been shown to have a superior performance in predicting the electricity load consumption. However, the consequences of using these methods have not fully been explored in terms of the various hidden layer structures, the depth of the model architecture, and the impact of tuning the model hyperparameters. In this paper, a systematic experimental methodology has been conducted to investigate the impact of using deep-stacked unidirectional (Uni-LSTM) and bidirectional (Bi-LSTM) networks on predicting electricity load consumption. In particular, two stacked configurations, which include two and three LSTM layers, are compared with the single-layered LSTM for both types to show the significant importance of adding the stacked layers. Moreover, for each proposed configuration, a hyperparameter optimization tool has been implemented to obtain the best model. The results indicate that the deep-stacked LSTM layers have no significant improvement in the prediction accuracy; nevertheless, they consume almost twice the time of the single-layered models. Also, the Bi-LSTM networks outperform the Uni-LSTM networks by 76.25%, 75.49%, and 75.35% in terms of Root Mean Square Error (RMSE), with respect to one, two, and three-layer model configurations, respectively. Furthermore, regarding the prediction accuracy comparison over the total tested period, the optimized Bi-LSTM model outperforms both the optimized Uni-LSTM model by 75.98%, 89.1%, and 89.37%, and the Support Vector Regression (SVR) model by 82.54%, 92.59%, and 92.89% in terms of (RMSE), the Mean Average Percentage Error (MAPE), and Mean Absolute Errors (MAE).

中文翻译：

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用于电力负荷预测的堆叠单向和双向长短期记忆网络的评估

摘要 电力负荷预测一直是电力系统管理过程中的一个重要问题。准确的预测模型对于避免可能对系统效率、经济性和可持续性产生负面影响的不精确预测至关重要。在几种预测技术中，深度学习方法，尤其是长短期记忆（LSTM），已被证明在预测电力负荷消耗方面具有优越的性能。然而，在各种隐藏层结构、模型架构的深度以及调整模型超参数的影响方面，尚未充分探索使用这些方法的后果。在本文中，已经进行了系统的实验方法来研究使用深堆叠单向 (Uni-LSTM) 和双向 (Bi-LSTM) 网络对预测电力负载消耗的影响。特别是，包括两个和三个 LSTM 层的两个堆叠配置与两种类型的单层 LSTM 进行了比较，以显示添加堆叠层的重要性。此外，对于每个提议的配置，已经实施了超参数优化工具以获得最佳模型。结果表明，deep-stacked LSTM层在预测精度上没有显着提高；尽管如此，它们消耗的时间几乎是单层模型的两倍。此外，Bi-LSTM 网络的性能比 Uni-LSTM 网络高 76.25%、75.49% 和 75%。就一层、二层和三层模型配置而言，均方根误差 (RMSE) 分别为 35%。此外，在整个测试期间的预测精度比较方面，优化后的 Bi-LSTM 模型优于优化的 Uni-LSTM 模型 75.98%、89.1% 和 89.37%，以及支持向量回归 (SVR) 模型的 82.54% 、 92.59% 和 92.89% (RMSE)、平均百分比误差 (MAPE) 和平均绝对误差 (MAE)。