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LSTM-based indoor air temperature prediction framework for HVAC systems in smart buildings


Accurate indoor air temperature (IAT) predictions for heating, ventilation, and air conditioning (HVAC) systems are challenging, especially for multi-zone building and for different HVAC system types. Moreover, the nonlinearity of the buildings thermal dynamics makes the IAT prediction more difficult since it is affected by complex factors such as controlled and uncontrolled points, outside weather conditions and occupancy schedule. This paper presents a long short-term memory (LSTM) model to predict IAT for multi-zone building based on direct multi-step prediction with sequence-to-sequence approach. Two strategies, LSTM-MISO and LSTM-MIMO, are built for multi-input single-output and multi-input multi-output, respectively. The performance of these two strategies has been evaluated based on two case studies on real smart buildings using variable air volume (VAV) and constant air volume (CAV) systems. For both buildings, experimental results showed that the LSTM models outperform multilayer perceptron models by reducing the prediction error by 50%.

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  1. Costa A, Keane MM, Torrens JI, Corry E (2013) Building operation and energy performance: monitoring, analysis and optimisation toolkit. Appl Energy 101:310–316

    Article  Google Scholar 

  2. Yang L, Yan H, Lam JC (2014) Thermal comfort and building energy consumption implications-a review. Appl Energy 115:164–173

    Article  Google Scholar 

  3. Pérez-Lombard L, Ortiz J, Pout C (2008) A review on buildings energy consumption information. Energy Build 40(3):394–398

    Article  Google Scholar 

  4. Baniasadi A, Habibi D, Bass O, Masoum MAS (2018) Optimal real-time residential thermal energy management for peak-load shifting with experimental verification. IEEE Trans Smart Grid 1:1

    Article  Google Scholar 

  5. Standard A (2017) Standard 55–2017 thermal environmental conditions for human occupancy. Ashrae, Atlanta

    Google Scholar 

  6. Rojas JD, Kunusch C, Ocampo-Martinez C, Puig V (2015) Control-oriented thermal modeling methodology for water-cooled pem fuel-cell-based systems. IEEE Trans Ind Electron 62(8):5146–5154

    Article  Google Scholar 

  7. Afroz Z, Urmee T, Shafiullah G, Higgins G (2018) Real-time prediction model for indoor temperature in a commercial building. Appl Energy 231:29–53

    Article  Google Scholar 

  8. Sturzenegger D, Gyalistras D, Morari M, Smith RS (2016) Model predictive climate control of a swiss office building: implementation, results, and cost-benefit analysis. IEEE Trans Control Syst Technol 24(1):1–12

    Article  Google Scholar 

  9. Chen X, Wang Q, Srebric J (2015) A data-driven state-space model of indoor thermal sensation using occupant feedback for low-energy buildings. Energy Build 91:187–198

    Article  Google Scholar 

  10. Huang H, Chen L, Hu E (2015) A neural network-based multi-zone modelling approach for predictive control system design in commercial buildings. Energy Build 97:86–97

    Article  Google Scholar 

  11. Serale G, Fiorentini M, Capozzoli A, Bernardini D, Bemporad A (2018) Model predictive control (mpc) for enhancing building and hvac system energy efficiency: problem formulation, applications and opportunities. Energies 11(3):631

    Article  Google Scholar 

  12. Huang H, Chen L, Hu E (2015) A new model predictive control scheme for energy and cost savings in commercial buildings: an airport terminal building case study. Build Environ 89:203–216

    Article  Google Scholar 

  13. Attoue N, Shahrour I, Younes R (2018) Smart building: use of the artificial neural network approach for indoor temperature forecasting. Energies 11(2):395

    Article  Google Scholar 

  14. Delcroix B, Le Ny J, Bernier M, Azam M, Qu B, Venne J-S (2020) Autoregressive neural networks with exogenous variables for indoor temperature prediction in buildings. Build Simul.

    Article  Google Scholar 

  15. He X, Zhang Z, Kusiak A (2014) Performance optimization of hvac systems with computational intelligence algorithms. Energy Build 81:371–380

    Article  Google Scholar 

  16. Zeng Y, Zhang Z, Kusiak A (2015) Predictive modeling and optimization of a multi-zone hvac system with data mining and firefly algorithms. Energy 86:393–402

    Article  Google Scholar 

  17. Xu C, Chen H, Wang J, Guo Y, Yuan Y (2019) Improving prediction performance for indoor temperature in public buildings based on a novel deep learning method. Build Environ 148:128–135

    Article  Google Scholar 

  18. Riekstin AC, Langevin A, Dandres T, Gagnon G, Cheriet M (2018) Time series-based ghg emissions prediction for smart homes. IEEE Trans Sustain Comput 1:1

    Google Scholar 

  19. Liang Y, Ouyang K, Jing L, Ruan S, Liu Y, Zhang J, Rosenblum DS, Zheng Y (2019) Urbanfm: inferring fine-grained urban flows, In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, 2019, pp 3132–3142

  20. Du Z, Fan B, Jin X, Chi J (2014) Fault detection and diagnosis for buildings and hvac systems using combined neural networks and subtractive clustering analysis. Build Environ 73:1–11

    Article  Google Scholar 

  21. Castilla M, Álvarez J, Ortega M, Arahal M (2013) Neural network and polynomial approximated thermal comfort models for hvac systems. Build Environ 59:107–115

    Article  Google Scholar 

  22. Abdel-Nasser M, Mahmoud K (2019) Accurate photovoltaic power forecasting models using deep lstm-rnn. Neural Comput Appl 31(7):2727–2740

    Article  Google Scholar 

  23. Jain A, Smarra F, Behl M, Mangharam R (2018) Data-driven model predictive control with regression trees-an application to building energy management. ACM Trans Cyber-Phys Syst 2(1):4

    Article  Google Scholar 

  24. Smarra F, Jain A, de Rubeis T, Ambrosini D, D’Innocenzo A, Mangharam R (2018) Data-driven model predictive control using random forests for building energy optimization and climate control. Appl Energy 226:1252–1272

    Article  Google Scholar 

  25. Javed A, Larijani H, Ahmadinia A, Emmanuel R (2014) Comparison of the robustness of rnn, mpc and ann controller for residential heating system. In: 2014 IEEE fourth international conference on big data and cloud computing. IEEE, 2014, pp 604–611

  26. Rahman A, Srikumar V, Smith AD (2018) Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks. Appl Energy 212:372–385

    Article  Google Scholar 

  27. Yan Y, Luh PB, Pattipati KR (2017) Fault diagnosis of hvac air-handling systems considering fault propagation impacts among components. IEEE Trans Autom Sci Eng 14(2):705–717

    Article  Google Scholar 

  28. Yao Y, Lian Z, Liu W, Hou Z, Wu M (2007) Evaluation program for the energy-saving of variable-air-volume systems. Energy Build 39(5):558–568

    Article  Google Scholar 

  29. Gers FA, Schraudolph NN, Schmidhuber J (2002) Learning precise timing with lstm recurrent networks. J Mach Learn Res 3:115–143

    MathSciNet  MATH  Google Scholar 

  30. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  31. Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Hasan M, Van Essen BC, Awwal AA, Asari VK (2019) A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3):292

    Article  Google Scholar 

  32. Lipton ZC, Kale DC, Elkan C, Wetzel R (2015) Learning to diagnose with lstm recurrent neural networks. arXiv:1511.03677

  33. Ashrae A (2002) Ashrae guideline 14: measurement of energy and demand savings. Am Soc Heat Refrig Air-Cond Eng 35:41–63

    Google Scholar 

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This work is supported by Mitacs Accelerate program and BrainBox AI.

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Correspondence to Fatma Mtibaa.

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Mtibaa, F., Nguyen, KK., Azam, M. et al. LSTM-based indoor air temperature prediction framework for HVAC systems in smart buildings. Neural Comput & Applic 32, 17569–17585 (2020).

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  • HVAC
  • LSTM
  • Sequence-to-sequence
  • Multi-step ahead predictions
  • VAV
  • CAV