Electric consumption prediction methods are investigated for many reasons such as decision-making related to energy efficiency as well as for anticipating demand in the energy market dynamics. The objective of the present work is the comparison between two Deep Learning models, namely the Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM) for univariate electric consumption Time Series (TS) short-term forecast. The Data Sets (DSs) were selected for their different contexts and scales, aiming the assessment of the models' robustness. Four DSs were used, related to the power consumption of: (a) a household in France; (b) a university building in Santar\'em, Brazil; (c) the T\'etouan city zones, in Morocco; and (c) the Singapore aggregated electric demand. The metrics RMSE, MAE, MAPE and R2 were calculated in a TS cross-validation scheme. The Friedman's test was applied to normalized RMSE (NRMSE) results, showing that BLSTM outperforms LSTM with statistically significant difference (p = 0.0455), corroborating the fact that bidirectional weight updating improves significantly the LSTM performance concerning different scales of electric power consumption.

翻译：电力消耗预测方法的研究源于多种需求，例如能源效率相关的决策制定以及能源市场动态中的需求预测。本研究的目标是对比两种深度学习模型——长短期记忆网络与双向长短期记忆网络在单变量电力消耗时间序列短期预测中的表现。数据集因背景与规模不同而选定，旨在评估模型的鲁棒性。研究使用了四个数据集，分别涉及以下电力消耗场景：（a）法国家庭；（b）巴西圣塔伦某大学建筑；（c）摩洛哥得土安市各区域；以及（d）新加坡总电力需求。在时间序列交叉验证框架下计算了均方根误差（RMSE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）和决定系数（R²）。对归一化RMSE结果应用弗里德曼检验，显示BLSTM在统计显著性差异（p = 0.0455）下优于LSTM，验证了双向权重更新能显著提升LSTM在不同电力消耗规模下的性能。