Comparing Long Short-Term Memory (LSTM) and bidirectional LSTM deep neural networks for power consumption prediction

Electric consumption prediction methods are investigated for many reasons, such as decision-making related to energy efficiency as well as for anticipating demand and the dynamics of the energy market. The objective of the present work is to compare two Deep Learning models, namely the Long Short-Term Memory (LSTM) model, and the Bi-directional LSTM (BLSTM) for univariate electric consumption Time Series (TS) short-term forecast model. The Data Sets (DSs) were selected for their different contexts and scales, with the goal of assessing the robustness of the models. Four DSs were used, related to the power consumption of: (a) a household in France; (b) a university building in Santare ' m, Brazil; (c) the Te ' touan city zones, in Morocco; and (d) the aggregated electric demand of Singapore. The metrics RMSE, MAE, MAPE and R2 were calculated in a TS cross-validation scheme. 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 significantly improves the LSTM performance with respect to different scales of electric power consumption. The present work provides statistical evidence supporting the conclusion that BLSTM outperforms LSTM models according to the tests performed, based on a complete methodology for TS prediction, and also establishes a baseline for future investigation of electric consumption TS prediction.