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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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    1. Wang, S. & Kim, A.A. & Johnson, E.M., 2017. "Understanding the deterministic and probabilistic business cases for occupant based plug load management strategies in commercial office buildings," Applied Energy, Elsevier, vol. 191(C), pages 398-413.
    2. Jia, Mengda & Srinivasan, Ravi S. & Raheem, Adeeba A., 2017. "From occupancy to occupant behavior: An analytical survey of data acquisition technologies, modeling methodologies and simulation coupling mechanisms for building energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 525-540.
    3. Staffell, Iain & Pfenninger, Stefan, 2018. "The increasing impact of weather on electricity supply and demand," Energy, Elsevier, vol. 145(C), pages 65-78.
    4. Hernández, Luis & Baladrón, Carlos & Aguiar, Javier M. & Carro, Belén & Sánchez-Esguevillas, Antonio & Lloret, Jaime, 2014. "Artificial neural networks for short-term load forecasting in microgrids environment," Energy, Elsevier, vol. 75(C), pages 252-264.
    5. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    6. Li, Nan & Li, Juncheng & Fan, Ruijuan & Jia, Hongyuan, 2015. "Probability of occupant operation of windows during transition seasons in office buildings," Renewable Energy, Elsevier, vol. 73(C), pages 84-91.
    7. Luis Hernández & Carlos Baladrón & Javier M. Aguiar & Lorena Calavia & Belén Carro & Antonio Sánchez-Esguevillas & Francisco Pérez & Ángel Fernández & Jaime Lloret, 2014. "Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems," Energies, MDPI, vol. 7(3), pages 1-23, March.
    8. Khayatian, Fazel & Sarto, Luca & Dall'O', Giuliano, 2017. "Building energy retrofit index for policy making and decision support at regional and national scales," Applied Energy, Elsevier, vol. 206(C), pages 1062-1075.
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    7. 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.
    8. Panchabikesan, Karthik & Haghighat, Fariborz & Mankibi, Mohamed El, 2021. "Data driven occupancy information for energy simulation and energy use assessment in residential buildings," Energy, Elsevier, vol. 218(C).
    9. Kuang-Sheng Liu & Iskandar Muda & Ming-Hung Lin & Ngakan Ketut Acwin Dwijendra & Gaylord Carrillo Caballero & Aníbal Alviz-Meza & Yulineth Cárdenas-Escrocia, 2023. "An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room," Sustainability, MDPI, vol. 15(2), pages 1-14, January.

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