Skip to main content

Extreme gradient boosting model based on improved Jaya optimizer applied to forecasting energy consumption in residential buildings

Abstract

A great number of prediction methods have been proposed in the past several decades for residential building energy consumption prediction. In this paper, the proposed machine learning model allows the prediction of the cooling and heating system load of residential buildings. These loads in this study were modelled as functions of eight input variables such as relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution.The model is based on eXtreme Gradient Boosting (XGBoost) which hyperparameters are adaptively tuned with a modified Jaya algorithm. The results of the proposed modified Jaya algorithm outperform the results of nine optimization metaheuristics to tune the XGBoost model based on tenfold cross-validation when applied to energy performance forecasting of residential buildings. It was also seen that XGBoost model applied to case of heating load obtained RMSE, determination coefficient R2 and MAE equals to 0.381, 0.998, and 0.2781, respectively, while that to cooling load the values were 0.9757, 0.989, and 0.612, respectively. Consequently, the XGBoost combined with the modified Jaya can be a reasonable tool for forecasting energy due to high accuracy, effectiveness and stability.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3

References

  1. Adam-Bourdarios C, Cowan G, Germain C, Guyon I, Kégl B, Rousseau D (2015) The Higgs boson machine learning challenge. In: 21st International Conference on Computing in High Energy and Nuclear Physics (CHEP2015), Journal of Physics: Conference Series 664, Okinawa, Japan

  2. AEO (2019) Annual Energy Outlook 2019. https://www.unenvironment.org/resources/emissions-gap-report-2018. Accessed July 2019

  3. Benavente-Peces C, Ibadah N (2020) Buildings energy efficiency analysis and classification using various machine learning technique classifiers. Energies 13, Article 3497

  4. Cao M-Y, Cao M-T (2014) Accurately predicting building energy performance using evolutionary multivariate adaptive regression splines. Appl Soft Comput 22:178–188

    Article  Google Scholar 

  5. Carrasco J, García S, Rueda MM, Das S, Herrera F (2020) Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review. Swarm Evol Comput 54, Article 100665

  6. Castelli M, Trujillo L, Vanneschia L, Popovic A (2015) Prediction of energy performance of residential buildings: agenetic programming approach. Energy Build 102:67–74

    Article  Google Scholar 

  7. Chakraborty D, Elzarka H (2019) Early detection of faults in HVAC systems using an XGBoost model with a dynamic threshold. Energy Build 185:326–344

    Article  Google Scholar 

  8. Chen K, Jiang J, Zheng F, Chen K (2018) A novel data-driven approach for residential electricity consumption prediction based on ensemble learning. Energy 150:49–60

    Article  Google Scholar 

  9. Chen T, Guestrin C (2016) XGBOOST: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’16, San Francisco, CA, USA, 785–794

  10. Coelho LS, Mariani VC, Goudos SK, Boursianis AD, Kokkinidis K, Kantartzis NV (2021) Chaotic jaya approaches to solving electromagnetic optimization benchmark problems. Telecom 2:222–231

    Article  Google Scholar 

  11. Foucquier A, Robert S, Suard F, Stéphan L, Jay A (2013) State of the art in building modelling and energy performances prediction: a review. Renew Sustain Energy Rev 23:272–288

    Article  Google Scholar 

  12. Freire RZ, Coelho LS, Santos GH, Mariani VC (2016) Predicting building’s corners hygrothermal behavior by using a fuzzy inference system combined with clustering and Kalman filter. Int Commun Heat Mass Transfer 71:225–233

    Article  Google Scholar 

  13. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232

    MathSciNet  Article  Google Scholar 

  14. Gilani S, O'Brien (2017) Review of current methods, opportunities, and challenges for in-situ monitoring to support occupant modelling in office spaces. Journal of Building Performance Simulation 10:444–470.

  15. Guo J, Yang L, Bie R, Yu J, Gao Y, Shene Y, Kos A (2019) An XGBoost-based physical fitness evaluation model using advanced feature selection and Bayesian hyper-parameter optimization for wearable running monitoring. Comput Netw 151:166–180

    Article  Google Scholar 

  16. Hastie T, Tibshirani R, Friedman J (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, Berlin, Germany, 2nd edition

  17. Houssein EH, Gad AG, Wazerv YM (2021) Jaya algorithm and applications: A comprehensive review. In: Razmjooy N et al (eds) Metaheuristics and Optimization in Computer and Electrical Engineering, Lecture Notes in Electrical Engineering 696, Springer, Germany

  18. IEA (2018) World Energy Outlook 2018—The gold standard of energy analysis. https://www.iea.org/weo2018/. Accessed July 2019

  19. Jaworski M, Duda P, Rutkowski L (2018) New splitting criteria for decision trees in stationary data streams. IEEE Trans Neural Netw Learn Syst 29:2516–2529

    MathSciNet  Article  Google Scholar 

  20. Kavaklioglu K (2018) Robust modeling of heating and cooling loads using partial least squares towards efficient residential building design. J Build Eng 18:467–475

    Article  Google Scholar 

  21. Kumar S, Pal SK, Singh R (2019) A novel hybrid model based on particle swarm optimisation and extreme learning machine for short-term temperature prediction using ambient sensors. Sustain Cities Soc 49, Article 101601

  22. Kwok SSK, Yuen RKK, Lee EWM (2011) An intelligent approach to assessing the effect of building occupancy on building cooling load prediction. Build Environ 46:1681–1690

    Article  Google Scholar 

  23. Mavrovouniotis M, Li C, Yang S (2017) A survey of swarm intelligence for dynamic optimization: Algorithms and applications. Swarm Evol Comput 33:1–17

    Article  Google Scholar 

  24. Natekin A, Knoll A (2013) Gradient Boosting Machines, a Tutorial. Front Neurorobot 7:21

    Article  Google Scholar 

  25. Nilashi M, Dalvi-Esfahani M, Ibrahim O, Mardani KBA, Zakuan N (2017) A soft computing method for the prediction of energy performance of residential buildings. Measurement 109:268–280

    Article  Google Scholar 

  26. Pang X, Wetter M, Bhattacharya P, Haves P (2012) A framework for simula- tion-based real-time whole building performance assessment. Build Environ 54:100–108

    Article  Google Scholar 

  27. Phobbo AE (2014) Machine learning wins the Higgs challenge. CERN Bull

  28. Pierezan J, Coelho LS (2018) Coyote optimization algorithm: A new metaheuristic for global optimization problems. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil, pp 2633–2640

  29. Pierezan J, Maidl G, Yamao EM, Coelho LS, Mariani VC (2019) Cultural coyote optimization algorithm applied to a heavy duty gas turbine operation. Energy Convers Manag 199, Article 111932

  30. Pistore L, Pernigotto G, Cappelletti F, Gasparella A, Romagnoni P (2019) A stepwise approach integrating feature selection, regression techniques and cluster analysis to identify primary retrofit interventions on large stocks of buildings. Sustain Cities Soc 47, Article 101438

  31. Precup RE, David RC, Roman RC, Petriu EM, Szedlak-Stinean AI (2021) Slime mould algorithm-based tuning of cost-effective fuzzy controllers for servo systems. Int J Comput Intell Syst 14:1042–1052

    Article  Google Scholar 

  32. Rahmi A, Mahmudy WF, Sarwani MZ (2020) Genetic algorithms for optimization of multi-level product distribution. Int J Artif Intell 18:135–147

    Google Scholar 

  33. Rao RV (2016) Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34

    Google Scholar 

  34. Rao RV, More KC, Coelho LS, Mariani VC (2017) Multi-objective optimization of the Stirling heat engine through self-adaptive Jaya algorithm. J Renew Sustain Energy 9:033703

    Article  Google Scholar 

  35. Ren Y, Zhang L, Suganthan PN (2016) Ensemble classification and regression—recent developments, applications and future directions. IEEE Comput Intell Mag 11:41–53

    Article  Google Scholar 

  36. Ren J, Cao SJ (2019) Incorporating online monitoring data into fast prediction models towards the development of artificial intelligent ventilation systems. Sustain Cities Soc 47, Article 101498

  37. Ribeiro GT, Mariani VC, Coelho LS (2019) Enhanced ensemble structures using wavelet neural networks applied to short-term load forecasting. Eng Appl Artif Intell 82:272–281

    Article  Google Scholar 

  38. Ribeiro MHD, Coelho LS (2019) Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series. Appl Soft Comput 86, Article 105837

  39. Rishee K, Jain RK, Smith KM, Culligan PJ, Taylor JE (2014) Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Appl Energy 123:168–178

    Article  Google Scholar 

  40. Roman RC, Precup RE, David RC (2018) Second order intelligent proportional-integral fuzzy control of twin rotor aerodynamic systems. Procedia Comput Sci 139:372–380

    Article  Google Scholar 

  41. Rutkowski L, Jaworski M, Pietruczuk L, Duda P (2015) A new method for data stream mining based on the misclassification error. IEEE Trans Neural Netw Learn Syst 26:1048–1059

    MathSciNet  Article  Google Scholar 

  42. Sala R, Müller R (2020) Benchmarking for metaheuristic black-box optimization: perspectives and open challenges. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK

  43. Seyedzadeh S, Rahimian FP, Rastogi P, Glesk I (2019) Tuning machine learning models for prediction of building energy loads. Sustain Cities Soc 47, Article 101484

  44. Simon D (2013) Evolutionary optimization algorithms. Wiley, Hoboken

    Google Scholar 

  45. Taieb SB, Hyndman TJ (2014) A gradient boosting approach to the Kaggle load forecasting competition. Int J Forecast 30:382–394

    Article  Google Scholar 

  46. Tsanas A, Xifara A (2012) Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy Build 49:560–567

    Article  Google Scholar 

  47. UNEP, 2018. Emissions Gap Report 2018. http://wedocs.unep.org/bitstream/handle/20.500.11822/26895/EGR2018_FullReport_EN.pdf. Accessed Dec 2019

  48. Vasconcelos Segundo EH, Mariani VC, Coelho LS (2019a) Design of heat exchangers using falcon optimization algorithm. Appl Therm Eng 156:119–144

    Article  Google Scholar 

  49. Vasconcelos Segundo, E. H., Mariani, V. C., Coelho, L. S. 2019b Metaheuristic inspired on owls behavior applied to heat exchangers design. Thermal Science and Engineering Progress, 14, Article 100431.

  50. Wolpert DH, Macready WG (1997) 1997 No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82

    Article  Google Scholar 

  51. Zapata H, Perozo N, Angulo W, Contreras J (2020) A hybrid swarm algorithm for collective construction of 3D structures. Int J Artif Intells 18:1–18

    Google Scholar 

  52. Zitar RA, Al-Betar MA, Awadallah MA, Doush IA, Assaleh K (2021) An intensive and comprehensive overview of Jaya algorithm, its versions and applications. Archives of Computational Methods in Engineering, Springer

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Viviana Cocco Mariani.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sauer, J., Mariani, V.C., dos Santos Coelho, L. et al. Extreme gradient boosting model based on improved Jaya optimizer applied to forecasting energy consumption in residential buildings. Evolving Systems (2021). https://doi.org/10.1007/s12530-021-09404-2

Download citation

Keywords

  • Building energy
  • Machine learning
  • Extreme gradient boosting
  • Energy simulation
  • Jaya algorithm