Enhanced household energy consumption forecasting using multivariate long short-term memory (LSTM) networks with weather data integration

Enhanced household energy consumption forecasting using multivariate long short-term memory (LSTM) networks with weather data integration

Forecasting household energy consumption is crucial for sustainable energy management and grid stability. Traditional models often struggle with complex data characteristics. This study introduces a TinyML-optimized multivariate LSTM model designed for resource-constrained environments, leveraging s...

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Bibliographic Details
Main Authors: Aditya Pai H, Kritika Kumari Mishra, Mahesh T R, J V Muruga Lal Jeyan, Anu Sayal
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Results in Engineering
Subjects:
TinyML
Edge computing
Smart home energy management
LSTM networks
Self-supervised learning in IoT
Energy consumption prediction
Online Access:http://www.sciencedirect.com/science/article/pii/S2590123025025812
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