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Occupant-Aware Energy Consumption Prediction in Smart Buildings Using a LSTM Model and Time Series Data

Author

Listed:
  • Muhammad Anan

    (College of Engineering, Alfaisal University, Riyadh 11533, Saudi Arabia)

  • Khalid Kanaan

    (Electrical and Computer Engineering Department, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia)

  • Driss Benhaddou

    (College of Engineering, Alfaisal University, Riyadh 11533, Saudi Arabia)

  • Nidal Nasser

    (College of Engineering, Alfaisal University, Riyadh 11533, Saudi Arabia)

  • Basheer Qolomany

    (Department of Internal Medicine, College of Medicine, Howard University, Washington, DC 20059, USA)

  • Hanaa Talei

    (School of Sciences and Engineering, Al-Akhawayn University, Ifrane 53000, Morocco)

  • Ahmad Sawalmeh

    (College of Engineering, Alfaisal University, Riyadh 11533, Saudi Arabia)

Abstract

Accurate energy consumption prediction in commercial buildings is a challenging research task. Energy prediction plays a crucial role in energy efficiency, management, planning, sustainability, risk management, diagnosis, and demand response. Although many studies have been conducted on building energy predictions, the impact of occupancy on energy prediction models for office-type commercial buildings remains insufficiently explored, despite its potential to improve energy efficiency by 20%. This study investigates energy prediction using a Long Short-Term Memory (LSTM) model that incorporates time-series power consumption data and considers occupancy. A real-world dataset containing the per-minute electricity consumption of various appliances in an office building in Houston, TX, USA, is utilized. The proposed machine learning models forecast future energy consumption based on hourly, 3-hourly, daily, and quarterly predictions for individual appliances and total energy usage. The model’s performance is evaluated using the following three metrics: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results demonstrate the superiority of the proposed system.

Suggested Citation

  • Muhammad Anan & Khalid Kanaan & Driss Benhaddou & Nidal Nasser & Basheer Qolomany & Hanaa Talei & Ahmad Sawalmeh, 2024. "Occupant-Aware Energy Consumption Prediction in Smart Buildings Using a LSTM Model and Time Series Data," Energies, MDPI, vol. 17(24), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:24:p:6451-:d:1549365
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    References listed on IDEAS

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    1. Wang, Zhi & Zhou, Huaichun & Peng, Xianyong & Cao, Shengxian & Tang, Zhenhao & Li, Kuangyu & Fan, Siyuan & Xue, Wenyuan & Yao, Guojia & Xu, Shiming, 2024. "A predictive model with time-varying delays employing channel equalization convolutional neural network for NOx emissions in flexible power generation," Energy, Elsevier, vol. 306(C).
    2. Hanaa Talei & Driss Benhaddou & Carlos Gamarra & Houda Benbrahim & Mohamed Essaaidi, 2021. "Smart Building Energy Inefficiencies Detection through Time Series Analysis and Unsupervised Machine Learning," Energies, MDPI, vol. 14(19), pages 1-21, September.
    3. Tianhe Sun & Tieyan Zhang & Yun Teng & Zhe Chen & Jiakun Fang, 2019. "Monthly Electricity Consumption Forecasting Method Based on X12 and STL Decomposition Model in an Integrated Energy System," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-16, October.
    4. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    5. Diogo M. F. Izidio & Paulo S. G. de Mattos Neto & Luciano Barbosa & João F. L. de Oliveira & Manoel Henrique da Nóbrega Marinho & Guilherme Ferretti Rissi, 2021. "Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters," Energies, MDPI, vol. 14(7), pages 1-19, March.
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