LSTM Networks for Home Energy Efficiency
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Severiche Maury, Zurisaddai; Arrubla Hoyos, Wilson; Ramírez-Velarde, Raúl; Cama Pinto, Dora; Holgado Terriza, Juan Antonio; Damas Hermoso, Miguel; Cama Pinto, AlejandroEditorial
MDPI
Materia
Home energy management system (HEMS) Artificial intelligence Deep learning
Date
2024-08-09Referencia bibliográfica
Severiche-Maury, Z.; Arrubla-Hoyos,W.; Ramirez-Velarde, R.; Cama-Pinto, D.; Holgado-Terriza, J.A.; Damas-Hermoso, M.; Cama-Pinto, A. LSTM Networks for Home Energy Efficiency. Designs 2024, 8, 78. https://doi.org/10.3390/designs8040078
Abstract
This study aims to develop and evaluate an LSTM neural network for predicting household
energy consumption. To conduct the experiment, a testbed was created consisting of five common
appliances, namely, a TV, air conditioner, fan, computer, and lamp, each connected to individual smart
meters within a Home Energy Management System (HEMS). Additionally, a meter was installed
on the distribution board to measure total consumption. Real-time data were collected at 15-min
intervals for 30 days in a residence that represented urban energy consumption in Sincelejo, Sucre,
inhabited by four people. This setup enabled the capture of detailed and specific energy consumption
data, facilitating data analysis and validating the system before large-scale implementation. Using
the detailed power consumption information of these devices, an LSTM model was trained to identify
temporal connections in power usage. Proper data preparation, including normalisation and feature
selection, was essential for the success of the model. The results showed that the LSTM model was
effective in predicting energy consumption, achieving a mean squared error (MSE) of 0.0169. This
study emphasises the importance of continued research on preferred predictive models and identifies
areas for future research, such as the integration of additional contextual data and the development of
practical applications for residential energy management. Additionally, it demonstrates the potential
of LSTM models in smart-home energy management and serves as a solid foundation for future
research in this field.