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Energy conservation in buildings through efficient A/C control using neural networks

Author

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  • Ben-Nakhi, Abdullatif E.
  • Mahmoud, Mohamed A.

Abstract

General regression neural networks (GRNNs) were used to optimize air conditioning setback scheduling in public buildings. To save energy, the temperature inside these buildings is allowed to rise after business hours by setting back the thermostat. The objective is to predict the time of the end of thermostat setback (EoS) such that the design temperature inside the building is restored in time for the start of business hours. State-of-the-art building simulation software, ESP-r, was used to generate a database that covered the past 5 years. The software was used to calculate EoS for two office buildings using the climate records in Kuwait. The EoS data for 1995 and 1996 were used for training and testing the neural networks (NNs). The robustness of the trained NN was tested by applying them to a "production" data set (1997-1999) which the networks have never "seen" before. A parametric study showed that the optimum GRNN design is one that uses a genetic adaptive algorithm, a so-called City Block distance metric, and a linear scaling function for the input data. External hourly temperature readings were used as network inputs, and the thermostat end of setback (EoS) is the output. The NN predictions were improved by developing a neural control-scheme. This scheme is based on using the temperature readings as they become available. Six NNs were designed and trained for this purpose. The performance of the NN analysis was evaluated using a statistical indicator (the coefficient of multiple determination), and by examination of the error patterns. The results show that the neural control-scheme is a powerful instrument for optimizing air conditioning setback scheduling based on external temperature records.

Suggested Citation

  • Ben-Nakhi, Abdullatif E. & Mahmoud, Mohamed A., 2002. "Energy conservation in buildings through efficient A/C control using neural networks," Applied Energy, Elsevier, vol. 73(1), pages 5-23, September.
  • Handle: RePEc:eee:appene:v:73:y:2002:i:1:p:5-23
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    Cited by:

    1. Xu, Xiaoqi & Culligan, Patricia J. & Taylor, John E., 2014. "Energy Saving Alignment Strategy: Achieving energy efficiency in urban buildings by matching occupant temperature preferences with a building’s indoor thermal environment," Applied Energy, Elsevier, vol. 123(C), pages 209-219.
    2. Antanasijević, Davor & Pocajt, Viktor & Ristić, Mirjana & Perić-Grujić, Aleksandra, 2015. "Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks," Energy, Elsevier, vol. 84(C), pages 816-824.
    3. K A H Kobbacy & S Vadera & M H Rasmy, 2007. "AI and OR in management of operations: history and trends," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(1), pages 10-28, January.
    4. Pang, Zhihong & Chen, Yan & Zhang, Jian & O'Neill, Zheng & Cheng, Hwakong & Dong, Bing, 2021. "How much HVAC energy could be saved from the occupant-centric smart home thermostat: A nationwide simulation study," Applied Energy, Elsevier, vol. 283(C).
    5. Du, Zhimin & Jin, Xinqiao & Yang, Yunyu, 2009. "Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network," Applied Energy, Elsevier, vol. 86(9), pages 1624-1631, September.
    6. Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
    7. Wong, S.L. & Wan, Kevin K.W. & Lam, Tony N.T., 2010. "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, Elsevier, vol. 87(2), pages 551-557, February.
    8. Yan, Huaxia & Pan, Yan & Li, Zhao & Deng, Shiming, 2018. "Further development of a thermal comfort based fuzzy logic controller for a direct expansion air conditioning system," Applied Energy, Elsevier, vol. 219(C), pages 312-324.
    9. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    10. Kazmi, Hussain & Suykens, Johan & Balint, Attila & Driesen, Johan, 2019. "Multi-agent reinforcement learning for modeling and control of thermostatically controlled loads," Applied Energy, Elsevier, vol. 238(C), pages 1022-1035.
    11. Yang, Lei & Nagy, Zoltan & Goffin, Philippe & Schlueter, Arno, 2015. "Reinforcement learning for optimal control of low exergy buildings," Applied Energy, Elsevier, vol. 156(C), pages 577-586.
    12. Lü, Xiaoshu & Lu, Tao & Kibert, Charles J. & Viljanen, Martti, 2014. "A novel dynamic modeling approach for predicting building energy performance," Applied Energy, Elsevier, vol. 114(C), pages 91-103.
    13. Kusiak, Andrew & Xu, Guanglin, 2012. "Modeling and optimization of HVAC systems using a dynamic neural network," Energy, Elsevier, vol. 42(1), pages 241-250.
    14. Ghoroghi, Ali & Petri, Ioan & Rezgui, Yacine & Alzahrani, Ateyah, 2023. "A deep learning approach to predict and optimise energy in fish processing industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 186(C).
    15. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    16. Goopyo Hong & Byungseon Sean Kim, 2018. "Development of a Data-Driven Predictive Model of Supply Air Temperature in an Air-Handling Unit for Conserving Energy," Energies, MDPI, vol. 11(2), pages 1-16, February.
    17. Jin Woo Moon & Sung Kwon Jung & Yong Oh Lee & Sangsun Choi, 2015. "Prediction Performance of an Artificial Neural Network Model for the Amount of Cooling Energy Consumption in Hotel Rooms," Energies, MDPI, vol. 8(8), pages 1-18, August.
    18. Michailidis, Iakovos T. & Schild, Thomas & Sangi, Roozbeh & Michailidis, Panagiotis & Korkas, Christos & Fütterer, Johannes & Müller, Dirk & Kosmatopoulos, Elias B., 2018. "Energy-efficient HVAC management using cooperative, self-trained, control agents: A real-life German building case study," Applied Energy, Elsevier, vol. 211(C), pages 113-125.
    19. Jin Woo Moon & Kyungjae Kim & Hyunsuk Min, 2015. "ANN-Based Prediction and Optimization of Cooling System in Hotel Rooms," Energies, MDPI, vol. 8(10), pages 1-21, September.
    20. Tzivanidis, C. & Antonopoulos, K.A. & Gioti, F., 2011. "Numerical simulation of cooling energy consumption in connection with thermostat operation mode and comfort requirements for the Athens buildings," Applied Energy, Elsevier, vol. 88(8), pages 2871-2884, August.
    21. Kusiak, Andrew & Li, Mingyang, 2010. "Cooling output optimization of an air handling unit," Applied Energy, Elsevier, vol. 87(3), pages 901-909, March.

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