IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i11p4749-d1661511.html
   My bibliography  Save this article

Predicting Energy Consumption and Time of Use of Home Appliances in an HEMS Using LSTM Networks and Smart Meters: A Case Study in Sincelejo, Colombia

Author

Listed:
  • Zurisaddai Severiche-Maury

    (Department of Electronic Engineering, Universidad de Sucre, Sincelejo 700001, Colombia)

  • Carlos Uc-Ríos

    (Faculty of Engineering, Universidad Autónoma de Campeche, Campeche 24039, Mexico
    Projects Department, Universidad Internacional Iberoamericana, Campeche 24000, Mexico)

  • Javier E. Sierra

    (Department of Electronic Engineering, Universidad de Sucre, Sincelejo 700001, Colombia)

  • Alejandro Guerrero

    (Department of Electronic Engineering, Universidad de Sucre, Sincelejo 700001, Colombia)

Abstract

Rising household electricity consumption, driven by technological advances and increased indoor activity, has led to higher energy costs and an increased reliance on non-renewable sources, exacerbating the carbon footprint. Home energy management systems (HEMS) are positioning themselves as an efficient alternative by integrating artificial intelligence to improve their accuracy. Predictive algorithms that provide accurate data on the future behavior of energy consumption and appliance usage time are required in these HEMS to achieve this goal. This study presents a predictive model based on recurrent neural networks with long short-term memory (LSTM), known to capture nonlinear relationships and long-term dependencies in time series data. The model predicts individual and total household energy consumption and appliance usage time. Training data were collected for 12 months from an HEMS installed in a typical Colombian house, using smart meters developed in this research. The model’s performance is evaluated using the mean squared error (MSE), reaching a value of 0.0168 kWh 2 . The results confirm the effectiveness of HEMS and demonstrate that the integration of LSTM-based predictive models can significantly improve energy efficiency and optimize household energy consumption.

Suggested Citation

  • Zurisaddai Severiche-Maury & Carlos Uc-Ríos & Javier E. Sierra & Alejandro Guerrero, 2025. "Predicting Energy Consumption and Time of Use of Home Appliances in an HEMS Using LSTM Networks and Smart Meters: A Case Study in Sincelejo, Colombia," Sustainability, MDPI, vol. 17(11), pages 1-26, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:4749-:d:1661511
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/11/4749/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/11/4749/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hamid R. Khosravani & María Del Mar Castilla & Manuel Berenguel & Antonio E. Ruano & Pedro M. Ferreira, 2016. "A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building," Energies, MDPI, vol. 9(1), pages 1-24, January.
    2. Fernando Dorado Rueda & Jaime Durán Suárez & Alejandro del Real Torres, 2021. "Short-Term Load Forecasting Using Encoder-Decoder WaveNet: Application to the French Grid," Energies, MDPI, vol. 14(9), pages 1-16, April.
    3. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    4. Xihui Chen & Dejun Ning, 2023. "FastInformer-HEMS: A Lightweight Optimization Algorithm for Home Energy Management Systems," Energies, MDPI, vol. 16(9), pages 1-17, May.
    5. Beungyong Park & Suh-hyun Kwon & Byoungchull Oh, 2024. "Standby Power Reduction of Home Appliance by the i-HEMS System Using Supervised Learning Techniques," Energies, MDPI, vol. 17(10), pages 1-12, May.
    6. Wei, Yixuan & Xia, Liang & Pan, Song & Wu, Jinshun & Zhang, Xingxing & Han, Mengjie & Zhang, Weiya & Xie, Jingchao & Li, Qingping, 2019. "Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks," Applied Energy, Elsevier, vol. 240(C), pages 276-294.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lian, Huihui & Ji, Ying & Niu, Menghan & Gu, Jiefan & Xie, Jingchao & Liu, Jiaping, 2025. "A hybrid load prediction method of office buildings based on physical simulation database and LightGBM algorithm," Applied Energy, Elsevier, vol. 377(PC).
    2. Jallal, Mohammed Ali & González-Vidal, Aurora & Skarmeta, Antonio F. & Chabaa, Samira & Zeroual, Abdelouhab, 2020. "A hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consumption prediction," Applied Energy, Elsevier, vol. 268(C).
    3. Yuwen You & Zhonghua Wang & Zhihao Liu & Chunmei Guo & Bin Yang, 2024. "Load Prediction of Regional Heat Exchange Station Based on Fuzzy Clustering Based on Fourier Distance and Convolutional Neural Network–Bidirectional Long Short-Term Memory Network," Energies, MDPI, vol. 17(16), pages 1-19, August.
    4. Dan Ling & Chaosong Li & Yan Wang & Pengye Zhang, 2022. "Fault Detection and Identification of Furnace Negative Pressure System with CVA and GA-XGBoost," Energies, MDPI, vol. 15(17), pages 1-19, August.
    5. Zurisaddai Severiche-Maury & Carlos Eduardo Uc-Rios & Wilson Arrubla-Hoyos & Dora Cama-Pinto & Juan Antonio Holgado-Terriza & Miguel Damas-Hermoso & Alejandro Cama-Pinto, 2025. "Forecasting Residential Energy Consumption with the Use of Long Short-Term Memory Recurrent Neural Networks," Energies, MDPI, vol. 18(5), pages 1-19, March.
    6. Myoungsoo Kim & Wonik Choi & Youngjun Jeon & Ling Liu, 2019. "A Hybrid Neural Network Model for Power Demand Forecasting," Energies, MDPI, vol. 12(5), pages 1-17, March.
    7. Masih Hosseinzadeh & Hossein Mashhadimoslem & Farid Maleki & Ali Elkamel, 2022. "Prediction of Solid Conversion Process in Direct Reduction Iron Oxide Using Machine Learning," Energies, MDPI, vol. 15(24), pages 1-25, December.
    8. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Effective non-intrusive load monitoring of buildings based on a novel multi-descriptor fusion with dimensionality reduction," Applied Energy, Elsevier, vol. 279(C).
    9. Qin, Meng & Hu, Wei & Qi, Xinzhou & Chang, Tsangyao, 2024. "Do the benefits outweigh the disadvantages? Exploring the role of artificial intelligence in renewable energy," Energy Economics, Elsevier, vol. 131(C).
    10. Vladimir Franki & Darin Majnarić & Alfredo Višković, 2023. "A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector," Energies, MDPI, vol. 16(3), pages 1-35, January.
    11. Marek Borowski & Klaudia Zwolińska, 2020. "Prediction of Cooling Energy Consumption in Hotel Building Using Machine Learning Techniques," Energies, MDPI, vol. 13(23), pages 1-19, November.
    12. Ding, Jiaqi & Zhao, Pu & Liu, Changjun & Wang, Xiaofang & Xie, Rong & Liu, Haitao, 2024. "From irregular to continuous: The deep Koopman model for time series forecasting of energy equipment," Applied Energy, Elsevier, vol. 364(C).
    13. García-Gusano, Diego & Suárez-Botero, Jasson & Dufour, Javier, 2018. "Long-term modelling and assessment of the energy-economy decoupling in Spain," Energy, Elsevier, vol. 151(C), pages 455-466.
    14. Yacouba Telly & Xuezhi Liu & Tadagbe Roger Sylvanus Gbenou, 2023. "Investigating the Growth Effect of Carbon-Intensive Economic Activities on Economic Growth: Evidence from Angola," Energies, MDPI, vol. 16(8), pages 1-18, April.
    15. Mahdi Asadi & Iman Larki & Mohammad Mahdi Forootan & Rouhollah Ahmadi & Meisam Farajollahi, 2023. "Long-Term Scenario Analysis of Electricity Supply and Demand in Iran: Time Series Analysis, Renewable Electricity Development, Energy Efficiency and Conservation," Sustainability, MDPI, vol. 15(5), pages 1-24, March.
    16. Asya İşçen & Kerem Öznacar & K. M. Murat Tunç & M. Erdem Günay, 2023. "Exploring the Critical Factors of Biomass Pyrolysis for Sustainable Fuel Production by Machine Learning," Sustainability, MDPI, vol. 15(20), pages 1-20, October.
    17. Chakraborty, Debaditya & Alam, Arafat & Chaudhuri, Saptarshi & Başağaoğlu, Hakan & Sulbaran, Tulio & Langar, Sandeep, 2021. "Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence," Applied Energy, Elsevier, vol. 291(C).
    18. Tien, Paige Wenbin & Wei, Shuangyu & Liu, Tianshu & Calautit, John & Darkwa, Jo & Wood, Christopher, 2021. "A deep learning approach towards the detection and recognition of opening of windows for effective management of building ventilation heat losses and reducing space heating demand," Renewable Energy, Elsevier, vol. 177(C), pages 603-625.
    19. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    20. Kostadin Yotov & Emil Hadzhikolev & Stanka Hadzhikoleva & Stoyan Cheresharov, 2022. "Neuro-Cybernetic System for Forecasting Electricity Consumption in the Bulgarian National Power System," Sustainability, MDPI, vol. 14(17), pages 1-18, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:4749-:d:1661511. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.