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Energy saving estimation for plug and lighting load using occupancy analysis

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  • Anand, Prashant
  • Cheong, David
  • Sekhar, Chandra
  • Santamouris, Mattheos
  • Kondepudi, Sekhar

Abstract

The gap between the actual and intended energy use for a building is often attributed to stochastic behaviour of occupants. This study systematically investigates the relationship of occupancy with plug and lighting loads energy consumption for several spaces of an institutional building floor. A new parameter ‘Energy-use per person (K)’ is introduced to explain the stochastic relationship between Energy and Occupancy. A model for K is developed as a function of occupancy using ‘Multiple non-linear regression (MNLR)’ and ‘Deep neural network (DNN)’ based algorithms. DNN algorithm shows a better prediction of K with less Mean absolute percentage error (MAPE) of 9.67% and 2.37% compared to 10.34% and 3.15% of MNLR for plug and lighting loads respectively. The model developed is used to estimate possible energy savings during occupied hours with a rule-based energy-use behaviour. Possible plug load energy savings are 8.9%, 3.1% and 1.3% for the classroom, open office, and computer room respectively. Similarly, possible lighting load energy savings are 65.1%, 43.6% and 38.4% for the classroom, open office, and computer room respectively. The study outcome, a robust and iterative ‘K model’ development process can be used as a support tool in decision making for facility management.

Suggested Citation

  • Anand, Prashant & Cheong, David & Sekhar, Chandra & Santamouris, Mattheos & Kondepudi, Sekhar, 2019. "Energy saving estimation for plug and lighting load using occupancy analysis," Renewable Energy, Elsevier, vol. 143(C), pages 1143-1161.
    • Handle: RePEc:eee:renene:v:143:y:2019:i:c:p:1143-1161
    DOI: 10.1016/j.renene.2019.05.089
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    References listed on IDEAS

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    2. Zhang, Chengyu & Luo, Zhiwen & Rezgui, Yacine & Zhao, Tianyi, 2024. "Enhancing building energy consumption prediction introducing novel occupant behavior models with sparrow search optimization and attention mechanisms: A case study for forty-five buildings in a univer," Energy, Elsevier, vol. 294(C).
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    6. Zini, Marco & Carcasci, Carlo, 2023. "Machine learning-based monitoring method for the electricity consumption of a healthcare facility in Italy," Energy, Elsevier, vol. 262(PB).
    7. Wei, Shuangyu & Tien, Paige Wenbin & Calautit, John Kaiser & Wu, Yupeng & Boukhanouf, Rabah, 2020. "Vision-based detection and prediction of equipment heat gains in commercial office buildings using a deep learning method," Applied Energy, Elsevier, vol. 277(C).
    8. Yu Cui & Zishang Zhu & Xudong Zhao & Zhaomeng Li, 2023. "Energy Schedule Setting Based on Clustering Algorithm and Pattern Recognition for Non-Residential Buildings Electricity Energy Consumption," Sustainability, MDPI, vol. 15(11), pages 1-23, May.
    9. Domenico Palladino & Iole Nardi & Cinzia Buratti, 2020. "Artificial Neural Network for the Thermal Comfort Index Prediction: Development of a New Simplified Algorithm," Energies, MDPI, vol. 13(17), pages 1-27, September.
    10. Fabiani, Claudia & Chiatti, Chiara & Pisello, Anna Laura, 2021. "Development of photoluminescent composites for energy efficiency in smart outdoor lighting applications: An experimental and numerical investigation," Renewable Energy, Elsevier, vol. 172(C), pages 1-15.
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