Preliminary studies: Comparing LSTM and BLSTM Deep Neural Networks for Power Consumption Prediction

TL;DR

This study compares LSTM and BLSTM neural networks for short-term electric power consumption forecasting across diverse datasets, finding BLSTM generally outperforms LSTM with statistical significance.

Contribution

It provides a comparative analysis of LSTM and BLSTM models on multiple real-world datasets, highlighting the superior performance of BLSTM in power consumption prediction.

Findings

BLSTM outperforms LSTM with statistical significance

Bidirectional weight updating improves prediction accuracy

Models tested across diverse geographic and scale datasets

Abstract

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.