Forecasting Residential Energy Consumption with the Use of Long Short-Term Memory Recurrent Neural Networks

Zurisaddai Severiche-Maury, Carlos Eduardo Uc-Rios, Wilson Arrubla-Hoyos, Dora Cama-Pinto (), Juan Antonio Holgado-Terriza, Miguel Damas-Hermoso and Alejandro Cama-Pinto ()
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Zurisaddai Severiche-Maury: Department of Electronic Engineering, Universidad de Sucre, Sincelejo 700002, Colombia
Carlos Eduardo Uc-Rios: Faculty of Engineering, Universidad Autónoma de Campeche, Campeche 24000, Mexico
Wilson Arrubla-Hoyos: Faculty of Engineering, Universidad Nacional Abierta y a Distancia, Sincelejo 700002, Colombia
Dora Cama-Pinto: Faculty of Industrial Engineering, Universidad Nacional Mayor de San Marcos, Lima 15081, Peru
Juan Antonio Holgado-Terriza: Software Engineering Department, Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, 18071 Granada, Spain
Miguel Damas-Hermoso: Department of Computer Engineering, Automation and Robotics, Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, 18071 Granada, Spain
Alejandro Cama-Pinto: Department of Computer Science and Electronics, Universidad de la Costa, Barranquilla 080002, Colombia

Energies, 2025, vol. 18, issue 5, 1-19

Abstract: In the quest to improve energy efficiency in residential environments, home energy management systems (HEMSs) have emerged as an effective solution, leveraging artificial intelligence (AI) technologies to improve energy efficiency. This study proposes a deep learning-based approach employing Long Short-Term Memory (LSTM) neural networks to predict household energy usage based on power consumption data from common appliances, such as lamps, fans, air conditioners, televisions, and computers. The model comprises two interrelated submodels: one predicts the individual energy consumption and usage time of each device, while the other estimates the total energy consumption of connected appliances. This dual structure enhances accuracy by capturing both device-specific consumption patterns and overall household energy use, facilitating informed decision-making at multiple levels. Following a systematic methodology that includes model building, training, and evaluation, the LSTM model achieved a low test set loss and mean squared error (MSE), with values of 0.0163 for individual consumption and usage time and 0.0237 for total consumption. Additionally, the predictive performance was strong, with MSE values of 1.0464 × 10 −6 for usage time, 0.0163 for individual consumption, and 0.0168 for total consumption. The analysis of scatter plots and residuals revealed a high degree of correspondence between predicted and actual values, validating the model’s accuracy and reliability in energy forecasting. This study represents a significant advancement in intelligent home energy management, contributing to improved efficiency and promoting sustainable consumption practices.

Keywords: HEMS; energy consumption prediction; deep learning; LSTM; networks; smart home energy efficiency (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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