Sheik Mohideen Shah1, Meganathan Selvamani1, Mahesh Thyluru Ramakrishna2,*, Surbhi Bhatia Khan3,4,5, Shakila Basheer6, Wajdan Al Malwi7, Mohammad Tabrez Quasim8
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2907-2926, 2025, DOI:10.32604/cmc.2025.063809
- 16 April 2025
Abstract 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… More >