  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.
