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A Demand Forecasting Strategy Based on a Retrofit Architecture for Remote Monitoring of Legacy Building Circuits

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  • Rubens A. Fernandes

    (Embedded Systems Laboratory, State University of Amazonas, Manaus 69050-020, Brazil
    Programa de Pós-Graduação em Engenharia Elétrica—PPGEE, Federal University of Pará, Belém 66075-110, Brazil)

  • Raimundo C. S. Gomes

    (Embedded Systems Laboratory, State University of Amazonas, Manaus 69050-020, Brazil
    Programa de Pós-Graduação em Engenharia Elétrica—PPGEE, Federal University of Pará, Belém 66075-110, Brazil)

  • Carlos T. Costa

    (Programa de Pós-Graduação em Engenharia Elétrica—PPGEE, Federal University of Pará, Belém 66075-110, Brazil)

  • Celso Carvalho

    (Departamento de Eletrônica e Computação—DTEC, Federal University of Amazonas, Manaus 69067-005, Brazil)

  • Neilson L. Vilaça

    (Embedded Systems Laboratory, State University of Amazonas, Manaus 69050-020, Brazil
    Programa de Pós-Graduação em Engenharia Elétrica—PPGEE, Federal University of Pará, Belém 66075-110, Brazil)

  • Lennon B. F. Nascimento

    (Embedded Systems Laboratory, State University of Amazonas, Manaus 69050-020, Brazil)

  • Fabricio R. Seppe

    (Embedded Systems Laboratory, State University of Amazonas, Manaus 69050-020, Brazil)

  • Israel G. Torné

    (Embedded Systems Laboratory, State University of Amazonas, Manaus 69050-020, Brazil)

  • Heitor L. N. da Silva

    (Embedded Systems Laboratory, State University of Amazonas, Manaus 69050-020, Brazil)

Abstract

Energy demand forecasting is crucial for planning and optimizing the use of energy resources in building facilities. However, integrating digital solutions and learning techniques into legacy buildings presents significant challenges due to limited or outdated resources, hampering predictive analytics in these buildings and their circuits. To fill this gap, this article proposes an innovative demand forecasting strategy using an AIoT retrofit architecture based on the SmartLVGrid metamodel. This architecture allows remote monitoring of legacy building circuits, facilitating the collection, processing and storage of data in the cloud. We use several learning algorithms, including linear regression, support vector regressor, random forest regressor, XGBoost regressor, and long short-term memory (LSTM) neural network, to predict energy demand 15 min ahead, identifying potential overruns of contracted demand in accordance with Brazilian regulations. After Bayesian optimization, the LSTM neural network outperformed other models for most of the selected datasets and detected 32 out of 38 demand overruns on the test set. XGBoost and random forest followed closely, detecting 30 demand overruns. Overall, our cost-effective solution optimizes energy usage and efficiently mitigates potential demand exceedances in building installations. This is achieved through a step-by-step approach to upgrading existing aging facilities, which promotes energy efficiency and sustainability.

Suggested Citation

  • Rubens A. Fernandes & Raimundo C. S. Gomes & Carlos T. Costa & Celso Carvalho & Neilson L. Vilaça & Lennon B. F. Nascimento & Fabricio R. Seppe & Israel G. Torné & Heitor L. N. da Silva, 2023. "A Demand Forecasting Strategy Based on a Retrofit Architecture for Remote Monitoring of Legacy Building Circuits," Sustainability, MDPI, vol. 15(14), pages 1-37, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11161-:d:1196262
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    References listed on IDEAS

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    1. Michal Pavlicko & Mária Vojteková & Oľga Blažeková, 2022. "Forecasting of Electrical Energy Consumption in Slovakia," Mathematics, MDPI, vol. 10(4), pages 1-20, February.
    2. Felipe Leite Coelho da Silva & Kleyton da Costa & Paulo Canas Rodrigues & Rodrigo Salas & Javier Linkolk López-Gonzales, 2022. "Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector," Energies, MDPI, vol. 15(2), pages 1-12, January.
    3. Rubens A. Fernandes & Raimundo C. S. Gomes & Ozenir Dias & Celso Carvalho, 2022. "A Novel Strategy for Smart Building Convergence Based on the SmartLVGrid Metamodel," Energies, MDPI, vol. 15(3), pages 1-26, January.
    4. Monika Zielińska-Sitkiewicz & Mariola Chrzanowska & Konrad Furmańczyk & Kacper Paczutkowski, 2021. "Analysis of Electricity Consumption in Poland Using Prediction Models and Neural Networks," Energies, MDPI, vol. 14(20), pages 1-21, October.
    5. Velasquez, Carlos E. & Zocatelli, Matheus & Estanislau, Fidellis B.G.L. & Castro, Victor F., 2022. "Analysis of time series models for Brazilian electricity demand forecasting," Energy, Elsevier, vol. 247(C).
    6. Ismail Shah & Faheem Jan & Sajid Ali & Tahir Mehmood, 2022. "Functional Data Approach for Short-Term Electricity Demand Forecasting," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-14, June.
    7. R. Claudio S. Gomes & Carlos Costa & Jose Silva & Jose Sicchar, 2019. "SmartLVGrid Platform—Convergence of Legacy Low-Voltage Circuits toward the Smart Grid Paradigm," Energies, MDPI, vol. 12(13), pages 1-23, July.
    8. Marwen Elkamel & Lily Schleider & Eduardo L. Pasiliao & Ali Diabat & Qipeng P. Zheng, 2020. "Long-Term Electricity Demand Prediction via Socioeconomic Factors—A Machine Learning Approach with Florida as a Case Study," Energies, MDPI, vol. 13(15), pages 1-21, August.
    9. Hyungah Lee & Dongju Kim & Jae-Hoi Gu, 2023. "Prediction of Food Factory Energy Consumption Using MLP and SVR Algorithms," Energies, MDPI, vol. 16(3), pages 1-21, February.
    10. Rubens A. Fernandes & Raimundo C. S. Gomes & Ozenir Dias & Celso Carvalho & Israel G. Torné & Jozias P. Oliveira & Carlos T. C. Júnior, 2022. "A Retrofit Strategy for Real-Time Monitoring of Building Electrical Circuits Based on the SmartLVGrid Metamodel," Energies, MDPI, vol. 15(23), pages 1-31, December.
    11. William Mounter & Chris Ogwumike & Huda Dawood & Nashwan Dawood, 2021. "Machine Learning and Data Segmentation for Building Energy Use Prediction—A Comparative Study," Energies, MDPI, vol. 14(18), pages 1-42, September.
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