Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities

  • Sina Ardabili
  • Amir MosaviEmail author
  • Annamária R. Várkonyi-Kóczy
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 101)


Building energy consumption plays an essential role in urban sustainability. The prediction of the energy demand is also of particular importance for developing smart cities and urban planning. Machine learning has recently contributed to the advancement of methods and technologies to predict demand and consumption for building energy systems. This paper presents a state of the art of machine learning models and evaluates the performance of these models. Through a systematic review and a comprehensive taxonomy, the advances of machine learning are carefully investigated and promising models are introduced.


Machine learning Smart cities IoT Deep learning Big data Soft computing Sustainable urban development Building energy Energy demand And consumption Sustainable cities 


Generalized boosted regression


Deep learning


Artificial neural network


Extreme learning machine


Machine learning


Support vector machine


Wavelet neural networks


Support vector regression


Genetic algorithm


Multi layered perceptron


Long short-term memory


Decision tree


Response surface methodology


Back propagation neural network


Centroid mean


Adaptive neuro fuzzy inference system


Analytic network process


Radial basis function


Feed-forward neural networks


Particle swarm optimization


Random forest


Non-random two-liquid


Recurrent neural network


Partial least squares


Discriminant analysis


Principal component analysis


Linear discriminant analysis


Autoregressive integrated moving average




Sparse Bayesian


Multi criteria decision making


Genetic programming


Multi linear regression


Step-wise Weight Assessment Ratio Analysis


Multi Objective Optimization by Ratio Analysis


Nonlinear autoregressive exogenous




This publication has been supported by the Project: “Support of research and development activities of the J. Selye University in the field of Digital Slovakia and creative industry” of the Research & Innovation Operational Programme (ITMS code: NFP313010T504) co-funded by the European Regional Development Fund.


  1. 1.
    Ahmad, T., et al.: Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment. Energy 158, 17–32 (2018)CrossRefGoogle Scholar
  2. 2.
    Jiang, Z., Lin, R., Yang, F.: A hybrid machine learning model for electricity consumer categorization using smart meter data. Energies 11(9) (2018)CrossRefGoogle Scholar
  3. 3.
    Kim, S.H., et al.: Deep learning based on multi-decomposition for short-term load forecasting. Energies 11(12) (2018)CrossRefGoogle Scholar
  4. 4.
    Laib, O., Khadir, M.T., Mihaylova, L.: Toward efficient energy systems based on natural gas consumption prediction with LSTM recurrent neural networks. Energy 177, 530–542 (2019)CrossRefGoogle Scholar
  5. 5.
    Li, Z., et al.: An ANN-based fast building energy consumption prediction method for complex architectural form at the early design stage. In: Building Simulation (2019)CrossRefGoogle Scholar
  6. 6.
    Liu, L., Ran, W.: Research on supply chain partner selection method based on BP neural network. Neural Comput. Appl. (2019)Google Scholar
  7. 7.
    Protić, M., Fathurrahman, F., Raos, M.: Modelling energy consumption of the republic of Serbia using linear regression and artificial neural network technique. Tehnicki Vjesnik 26(1), 135–141 (2019)Google Scholar
  8. 8.
    Rahman, A., Smith, A.D.: Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms. Appl. Energy 228, 108–121 (2018)CrossRefGoogle Scholar
  9. 9.
    Seyedzadeh, S., et al.: Machine learning for estimation of building energy consumption and performance: a review. Visualization Eng. 6(1) (2018)Google Scholar
  10. 10.
    Mosavi, A., Ozturk, P., Chau, K.W.: Flood prediction using machine learning models: literature review. Water (Switzerland) 10(11) (2018)CrossRefGoogle Scholar
  11. 11.
    Mosavi, A., Rabczuk, T.: Learning and intelligent optimization for material design innovation. In: Kvasov, D.E., et al. (eds.), pp. 358–363. Springer (2017)Google Scholar
  12. 12.
    Mosavi, A., Rabczuk, T., Várkonyi-Kóczy, A.R.: Reviewing the novel machine lea0072ning tools for materials design. Luca, D., Sirghi, L., Costin, C., (eds.), pp. 50–58. Springer (2018)Google Scholar
  13. 13.
    Mosavi, A., et al.: State of the art of machine learning models in energy systems, a systematic review. Energies 12(7) (2019)CrossRefGoogle Scholar
  14. 14.
    Mosavi, A., et al.: Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning. Eng. Appl. Comput. Fluid Mech. 13(1), 482–492 (2019)Google Scholar
  15. 15.
    Mosavi, A., Várkonyi-Kóczy, A.R.: Integration of machine learning and optimization for robot learning. Jablonski, R., Szewczyk, R., (eds.), pp. 349–355. Springer (2017)Google Scholar
  16. 16.
    Nosratabadi, S., et al.: Sustainable business models: a review. Sustainability (Switzerland) 11(6) (2019)CrossRefGoogle Scholar
  17. 17.
    Qasem, S.N., et al.: Estimating daily dew point temperature using machine learning algorithms. Water (Switzerland) 11(3) (2019)CrossRefGoogle Scholar
  18. 18.
    Rezakazemi, M., Mosavi, A., Shirazian, S.: ANFIS pattern for molecular membranes separation optimization. J. Mol. Liq. 274, 470–476 (2019)CrossRefGoogle Scholar
  19. 19.
    Riahi-Madvar, H., et al.: Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry. Eng. Appl. Comput. Fluid Mech. 13(1), 529–550 (2019)Google Scholar
  20. 20.
    Shabani, S., Samadianfard, S., Taghi Sattari, M., Shamshirband, S., Mosavi, A., Kmet, T., Várkonyi-Kóczy, A.R.: Modeling daily pan evaporation in humid cli-mates Using Gaussian Process Regression (2019). arXiv:2019070351.
  21. 21.
    Shamshirband, S., Hadipoor, S., Baghban, M., Mosavi, A., Bukor, A., Annamaria, J., Varkonyi-Koczy, R.: Developing an ANFIS-PSO model to predict mercury emissions in Combustion Flue Gases (2019). arXiv:2019070165.
  22. 22.
    Shamshirband, S., et al.: Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters. Eng. Appl. Comput. Fluid Mech 13(1), 91–101 (2019)Google Scholar
  23. 23.
    Shamshirband, S., Mosavi, A., Rabczuk, T.: Particle swarm optimization model to predict scour depth around bridge pier (2019). arXiv:1906.08863
  24. 24.
    Taherei Ghazvinei, P., et al.: Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network. Eng. Appl. Comput. Fluid Mech 12(1), 738–749 (2018)Google Scholar
  25. 25.
    Torabi, M., et al.: A Hybrid clustering and classification technique for forecasting short-term energy consumption. Environ. Prog. Sustain. Energy 38(1), 66–76 (2019)CrossRefGoogle Scholar
  26. 26.
    Torabi, M., et al.: A hybrid machine learning approach for daily prediction of solar radiation. In: Lecture Notes in Networks and Systems, pp. 266–274. Springer (2019)Google Scholar
  27. 27.
    Aram, F., et al.: Design and validation of a computational program for analysing mental maps: aram mental map analyzer. Sustainability (Switzerland) 11(14) (2019)CrossRefGoogle Scholar
  28. 28.
    Asadi, E., et al.: Groundwater Quality Assessment For Drinking And Agricultural Purposes In Tabriz Aquifer, Iran (2019)Google Scholar
  29. 29.
    Asghar, M.Z., Subhan, F., Imran, M., Kundi, F.M., Shamshirband, S., Mosavi, A., Csiba, P., Várkonyi-Kóczy, A.R.: Performance evaluation of supervised machine learning techniques for efficient detection of emotions from online content (2019). arXiv:2019080019.
  30. 30.
    Bemani, A., Baghban, A., Shamshirband, S., Mosavi, A., Csiba, P., Várkonyi-Kóczy, A.R.: Applying ANN, ANFIS, and LSSVM models for estimation of Acid Sol-vent solubility in Supercritical CO2 (2019). arXiv:2019060055
  31. 31.
    Choubin, B., et al.: Snow avalanche hazard prediction using machine learning methods. J. Hydrol. 577 (2019)CrossRefGoogle Scholar
  32. 32.
    Choubin, B., et al.: An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Sci. Total Environ. 651, 2087–2096 (2019)CrossRefGoogle Scholar
  33. 33.
    Dehghani, M., et al.: Prediction of hydropower generation using Grey wolf optimization adaptive neuro-fuzzy inference system. Energies 12(2) (2019)CrossRefGoogle Scholar
  34. 34.
    Dineva, A., et al.: Review of soft computing models in design and control of rotating electrical machines. Energies 12(6) (2019)CrossRefGoogle Scholar
  35. 35.
    Dineva, A., et al.: Multi-label classification for fault diagnosis of rotating electrical machines (2019)Google Scholar
  36. 36.
    Farzaneh-Gord, M., et al.: Numerical simulation of pressure pulsation effects of a snubber in a CNG station for increasing measurement accuracy. Eng. Appl. Comput. Fluid Mech 13(1), 642–663 (2019)Google Scholar
  37. 37.
    Ghalandari, M., et al.: Investigation of submerged structures’ flexibility on sloshing frequency using a boundary element method and finite element analysis. Eng. Appl. Comput. Fluid Mech 13(1), 519–528 (2019)Google Scholar
  38. 38.
    Ghalandari, M., et al.: Flutter speed estimation using presented differential quadrature method formulation. Eng. Appl. Comput. Fluid Mech 13(1), 804–810 (2019)Google Scholar
  39. 39.
    Karballaeezadeh, N., et al.: Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan-Firuzkuh road). Eng. Appl. Comput. Fluid Mech 13(1), 188–198 (2019)Google Scholar
  40. 40.
    Menad, N.A., et al.: Modeling temperature dependency of oil-water relative permeability in thermal enhanced oil recovery processes using group method of data handling and gene expression programming. Eng. Appl. Comput. Fluid Mech 13(1), 724–743 (2019)MathSciNetGoogle Scholar
  41. 41.
    Mohammadzadeh, S., et al.: Prediction of compression index of fine-grained soils using a gene expression programming model. Infrastructures 4(2), 26 (2019)CrossRefGoogle Scholar
  42. 42.
    Mosavi, A., Edalatifar, M.: A hybrid neuro-fuzzy algorithm for prediction of reference evapotranspiration. In: Lecture Notes in Networks and Systems, pp. 235–243. Springer (2019)Google Scholar
  43. 43.
    Mosavi, A., Lopez, A., Várkonyi-Kóczy, A.R.: Industrial applications of big data: state of the art survey. Luca, D., Sirghi, L., Costin, C., (eds.), pp. 225–232. Springer (2018)Google Scholar
  44. 44.
    Osborne, P.E., Alvares-Sanches, T.: Quantifying how landscape composition and configuration affect urban land surface temperatures using machine learning and neutral landscapes. Comput. Environ. Urban Syst. 76, 80–90 (2019)CrossRefGoogle Scholar
  45. 45.
    Djenouri, D., et al.: Machine learning for smart building applications: review and taxonomy. ACM Comput. Surv. 52(2) (2019)CrossRefGoogle Scholar
  46. 46.
    Hribar, R., et al.: A comparison of models for forecasting the residential natural gas demand of an urban area. Energy, 511–522 (2019)CrossRefGoogle Scholar
  47. 47.
    Singaravel, S., Suykens, J., Geyer, P.: Deep-learning neural-network architectures and methods: using component-based models in building-design energy prediction. Adv. Eng. Inform. 38, 81–90 (2018)CrossRefGoogle Scholar
  48. 48.
    Ahmad, T., et al.: A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: a review. Energy Build. 165, 301–320 (2018)CrossRefGoogle Scholar
  49. 49.
    Raza, M.Q., Nadarajah, M., Ekanayake, C.: Demand forecast of PV integrated bioclimatic buildings using ensemble framework. Appl. Energy 208, 1626–1638 (2017)CrossRefGoogle Scholar
  50. 50.
    Sharif, S.A., Hammad, A.: Developing surrogate ANN for selecting near-optimal building energy renovation methods considering energy consumption, LCC and LCA. J. Build. Eng. 25 (2019)CrossRefGoogle Scholar
  51. 51.
    Chammas, M., Makhoul, A., Demerjian, J.: An efficient data model for energy prediction using wireless sensors. Comput. Electr. Eng. 76, 249–257 (2019)CrossRefGoogle Scholar
  52. 52.
    Fenza, G., Gallo, M., Loia, V.: Drift-aware methodology for anomaly detection in smart grid. IEEE Access 7, 9645–9657 (2019)CrossRefGoogle Scholar
  53. 53.
    Almalaq, A., Zhang, J.J.: Evolutionary deep learning-based energy consumption prediction for buildings. IEEE Access 7, 1520–1531 (2019)CrossRefGoogle Scholar
  54. 54.
    Chou, J.S., Tran, D.S.: Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders. Energy, 709–726 (2018)CrossRefGoogle Scholar
  55. 55.
    Koschwitz, D., Frisch, J., van Treeck, C.: Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX recurrent neural network: a comparative study on district scale. Energy 165, 134–142 (2018)CrossRefGoogle Scholar
  56. 56.
    Ardabili, S., Mosavi, A., Mahmoudi, Mesri Gundoshmian, T, Nosratabadi, S., Var-konyi-Koczy, A.: Modelling temperature variation of mushroom growing hall us-ing artificial neural networks (2019)Google Scholar
  57. 57.
    Mesri Gundoshmian, T., Ardabili, S., Mosavi, A., Varkonyi-Koczy, A.: Prediction of combine harvester performance using hybrid machine learning modeling and re-sponse surface methodology (2019)Google Scholar
  58. 58.
    Ardabili, S., Mosavi, A., Varkonyi-Koczy, A.: Systematic review of deep learning and machine learning models in biofuels research (2019)Google Scholar
  59. 59.
    Ardabili, S., Mosavi, A., Varkonyi-Koczy, A.: Advances in machine learning model-ing reviewing hybrid and ensemble methods (2019)Google Scholar
  60. 60.
    Ardabili, S., Mosavi, A., Varkonyi-Koczy, A.: Building energy information: demand and consumption prediction with machine learning models for sustainable and smart cities (2019)Google Scholar
  61. 61.
    Ardabili, S., Mosavi, A., Dehghani, M., Varkonyi-Koczy, A.: Deep learning and machine learning in hydrological processes climate change and earth systems a systematic review (2019)Google Scholar
  62. 62.
    Mohammadzadeh D., Karballaeezadeh, N., Mohemmi, M., Mosavi, A., Varkonyi-Koczy A.: Urban train soil-structure interaction modeling and analysis (2019)Google Scholar
  63. 63.
    Mosavi, A., Ardabili, S., Varkonyi-Koczy, A.: List of deep learning models (2019)Google Scholar
  64. 64.
    Nosratabadi, S., Mosavi, A., Keivani, R., Ardabili, S., Aram, F.: State of the art sur-vey of deep learning and machine learning models for smart cities and urban sustainability (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Advanced Studies KoszegKoszegHungary
  2. 2.Kalman Kando Faculty of Electrical EngineeringObuda UniversityBudapestHungary
  3. 3.School of the Built EnvironmentOxford Brookes UniversityOxfordUK
  4. 4.Department of Mathematics and InformaticsJ. Selye UniversityKomarnoSlovakia

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