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Occupant-centric miscellaneous electric loads prediction in buildings using state-of-the-art deep learning methods

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  • Das, Anooshmita
  • Annaqeeb, Masab Khalid
  • Azar, Elie
  • Novakovic, Vojislav
  • Kjærgaard, Mikkel Baun

Abstract

Buildings have emerged as one of the dominant sectors when it comes to worldwide energy consumption. While a large portion of this consumption is due to the Heating, Ventilation, and Air Conditioning (HVAC) loads, a significant portion is contributed through the use of standard equipment, also known as Miscellaneous Electric Loads (MEL). It is necessary to understand the consumption patterns to optimize the MELs of the occupants using the building and conduct accurate forecasts for building energy management. One of the methods to achieve that purpose is the employment of Deep Learning (DL) methods. This study provides an analysis using Long Short-Term Memory (LSTM) model as a baseline for predicting MELs. The predictions were conducted for a day-ahead and a week-ahead period. Furthermore, the results from the baseline model were then used in a comparative analysis with two other state-of-the-art DL models; Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Units (GRU).

Suggested Citation

  • Das, Anooshmita & Annaqeeb, Masab Khalid & Azar, Elie & Novakovic, Vojislav & Kjærgaard, Mikkel Baun, 2020. "Occupant-centric miscellaneous electric loads prediction in buildings using state-of-the-art deep learning methods," Applied Energy, Elsevier, vol. 269(C).
    • Handle: RePEc:eee:appene:v:269:y:2020:i:c:s0306261920306474
    DOI: 10.1016/j.apenergy.2020.115135
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    Cited by:

    1. Xu, Xiaoxiao & Yu, Hao & Sun, Qiuwen & Tam, Vivian W.Y., 2023. "A critical review of occupant energy consumption behavior in buildings: How we got here, where we are, and where we are headed," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    2. Zhang, Chengyu & Ma, Liangdong & Luo, Zhiwen & Han, Xing & Zhao, Tianyi, 2024. "Forecasting building plug load electricity consumption employing occupant-building interaction input features and bidirectional LSTM with improved swarm intelligent algorithms," Energy, Elsevier, vol. 288(C).
    3. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    4. Wang, Jianzhou & Zhang, Linyue & Li, Zhiwu, 2022. "Interval forecasting system for electricity load based on data pre-processing strategy and multi-objective optimization algorithm," Applied Energy, Elsevier, vol. 305(C).
    5. Haizhou Fang & Hongwei Tan & Ningfang Dai & Zhaohui Liu & Risto Kosonen, 2023. "Hourly Building Energy Consumption Prediction Using a Training Sample Selection Method Based on Key Feature Search," Sustainability, MDPI, vol. 15(9), pages 1-23, May.
    6. Song, Yuguang & Xia, Mingchao & Chen, Qifang & Chen, Fangjian, 2023. "A data-model fusion dispatch strategy for the building energy flexibility based on the digital twin," Applied Energy, Elsevier, vol. 332(C).
    7. Botman, Lola & Lago, Jesus & Fu, Xiaohan & Chia, Keaton & Wolf, Jesse & Kleissl, Jan & De Moor, Bart, 2024. "Building plug load mode detection, forecasting and scheduling," Applied Energy, Elsevier, vol. 364(C).
    8. Dalia Mohammed Talat Ebrahim Ali & Violeta Motuzienė & Rasa Džiugaitė-Tumėnienė, 2024. "AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings," Energies, MDPI, vol. 17(17), pages 1-35, August.
    9. 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).

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