TY  - EJOU
AU  - Shah, Sheik Mohideen 
AU  - Selvamani, Meganathan 
AU  - Ramakrishna, Mahesh Thyluru 
AU  - Khan, Surbhi Bhatia 
AU  - Basheer, Shakila 
AU  - Malwi, Wajdan Al 
AU  - Quasim, Mohammad Tabrez 

TI  - Bidirectional LSTM-Based Energy Consumption Forecasting: Advancing AI-Driven Cloud Integration for Cognitive City Energy Management
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 2
SN  - 1546-2226

AB  - Efficient energy management is a cornerstone of advancing cognitive cities, where AI, IoT, and cloud computing seamlessly integrate to meet escalating global energy demands. Within this context, the ability to forecast electricity consumption with precision is vital, particularly in residential settings where usage patterns are highly variable and complex. This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory (LSTM) network. Leveraging a dataset containing over two million multivariate, time-series observations collected from a single household over nearly four years, our model addresses the limitations of traditional time-series forecasting methods, which often struggle with temporal dependencies and non-linear relationships. The bidirectional LSTM architecture processes data in both forward and backward directions, capturing past and future contexts at each time step, whereas existing unidirectional LSTMs consider only a single temporal direction. This design, combined with dropout regularization, leads to a 20.6% reduction in RMSE and an 18.8% improvement in MAE over conventional unidirectional LSTMs, demonstrating a substantial enhancement in prediction accuracy and robustness. Compared to existing models—including SVM, Random Forest, MLP, ANN, and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing, significantly outperforming these benchmarks. These results highlight the model’s superior ability to navigate the complexities of energy usage patterns, reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive cities. By integrating advanced machine learning techniques with IoT and cloud infrastructure, this research contributes to the development of intelligent, sustainable urban environments.
KW  - Deep learning; bidirectional LSTM; energy consumption forecasting; time-series analysis; predictive modeling; machine learning in energy management

DO  - 10.32604/cmc.2025.063809