Mirko Stojiljković, Marko Ignjatović, Goran Vučković

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Primary energy is the quantity often used to express the total amount of consumed or saved energy. It is especially important for certification and standards compliance evaluation of buildings. In order to accurately assess primary energy consumption and savings of building retrofit measures, one needs adequate models that can be based on the knowledge of phenomena, on the collected data, or both. This paper analyzes the learning performance and accuracy of data-driven models based on decision trees that predict primary energy savings related to building retrofit measures. It uses supervised machine learning methods — regression decision trees and ensemble methods based on them — to train, validate, and test such models. Ensemble methods based on decision trees are powerful, accurate, but also convenient to prepare and fast to train. In addition, they calculate the relative importance of each feature. The research results with highly accurate data-driven models and consistent feature importance values.


buildings, data-driven models, ensemble methods, primary energy savings, supervised machine learning

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****: Directive (EU) 2018/2002 of the European Parliament And of the Council of 11 December 2018 amending Directive 2012/27/EU on energy efficiency (Text with EEA relevance), Official Journal of the European Union, L 328, pp. 210–230, 2018.

****: Directive (EU) 2018/844 of the European Parliament and of the Council of 30 May 2018 amending Directive 2010/31/EU on the energy performance of buildings and Directive 2012/27/EU on energy efficiency (Text with EEA relevance), Official Journal of the European Union, L 156, pp. 75–91, 2018.

Deb, C. et al.: A review on time series forecasting techniques for building energy consumption, Renewable and Sustainable Energy Reviews, Vol 74, pp. 902–924, 2017.

Foucquier, A. et al.: State of the art in building modelling and energy performances prediction: A review, Renewable and Sustainable Energy Reviews, Vol. 23, pp. 272–288, 2013.

Seyedzadeh, S. et al.: Tuning machine learning models for prediction of building energy loads, Sustainable Cities and Society Vol. 47, 101484, pp. 1–18, 2019.

Mosavi, A. et al.: State of the Art of Machine Learning Models in Energy Systems, a Systematic Review, Energies, Vol. 12, No. 7, 1301, pp. 1–42, 2019.

Rahman, A. and Smith, A.: Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms, Applied Energy, Vol. 228, pp. 108–121, 2018.

Sretenović, A. A. et al.: Support Vector Machine for the Prediction of Heating Energy Use, Thermal Science, Vol. 22, Suppl. 4, pp. S1171–S1181, 2018.

Bui, D.-K. et al.: An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings, Energy, Vol. 190, 116370, pp. 1–12, 2020.

Rahman, A. et al.: Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks, Applied Energy, Vol. 212, pp. 372–385, 2018.

Zeng, A. et al.: Prediction of building electricity usage using Gaussian Process Regression, Journal of Building Engineering, Vol. 28, 101054, pp. 1–8, 2020.

Cui, B. et al.: A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses, Applied Energy, Vol. 236, pp. 101–116, 2019.

Mawson, V. J. and Hughes, B. R.: Deep learning techniques for energy forecasting and condition monitoring in the manufacturing sector, Energy & Buildings, Vol. 217, 109966, pp. 1–10, 2020.

Dai, X. et al.: A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings, Energy & Buildings, Vol. 223, 110159, pp. 1–15, 2020.

Huchuk, B. et al.: Comparison of machine learning models for occupancy prediction in residential buildings using connected thermostat data, Building and Environment, Vol. 160, 106177, pp. 1–12, 2019.

Jin, X. et al.: Foresee: A user-centric home energy management system for energy efficiency and demand response, Applied Energy, Vol. 205, pp. 1583–1595, 2017.

Ngarambe, J. et al.: The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: energy implications of AI-based thermal comfort controls, Energy & Buildings, Vol. 211, 109807, pp. 1–15, 2020.

Chaudhuri, T. et al.: A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings, Applied Energy, Vol. 248, pp. 44–53, 2019.

Asadi, E. et al.: Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application, Energy and Buildings, Vol. 81, pp. 444–456, 2014.

Ascione, F. et al.: CASA, cost-optimal analysis by multi-objective optimisation and artificial neural networks: A new framework for the robust assessment of cost-optimal energy retrofit, feasible for any building, Energy and Buildings, Vol. 146, pp. 200–219, 2017.

Chen, X., and Yang, H.: A multi-stage optimization of passively designed high-rise residential buildings in multiple building operation scenarios, Applied Energy, Vol. 206, pp. 541–557, 2017.

Gossard, D. et al.: Multi-objective optimization of a building envelope for thermal performance using genetic algorithms and artificial neural network, Energy and Buildings, Vol. 67, pp. 253–260, 2013.

Sharif, S. A. and Hammad, A.: Developing surrogate ANN for selecting near-optimal building energy renovation methods considering energy consumption, LCC and LCA, Journal of Building Engineering, Vol. 25, 100790, pp. 1–15, 2019.

Domahidi, A. et al.: Learning decision rules for energy efficient building control, Journal of Process Control, Vol. 24, pp. 763–772, 2014.

Drgoňa, J. et al.: Approximate model predictive building control via machine learning, Applied Energy, Vol. 218, pp. 199–216, 2018.

Pallonetto, F. et al.: Demand response algorithms for smart-grid ready residential buildings using machine learning models, Applied Energy, Vol. 239, pp. 1265–1282, 2019.

Smarra, F.: Data-driven model predictive control using random forests for building energy optimization and climate control, Applied Energy, Vol. 226, pp. 1252–1272, 2018.

Wang, J. et al.: Data-driven model predictive control for building climate control: Three case studies on different buildings, Building and Environment, Vol. 160, 106204, pp. 1–12, 2019.

Yang, S. et al.: Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization, Applied Energy, Vol. 271, 115147, pp. 1–16, 2020.

Miller, C. et al.: A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings, Renewable and Sustainable Energy Reviews, Vol. 81, pp. 1365–1377, 2018

Ding, Y. et al.: Effect of input variables on cooling load prediction accuracy of an office building, Applied Thermal Engineering, Vol. 128, pp. 225–234, 2018.

Naganathan, H. et al.: Building energy modeling (BEM) using clustering algorithms and semi-supervised machine learning approaches, Automation in Construction, Vol. 72, pp. 187–194, 2016.

Shang, C., and You, F.: A data-driven robust optimization approach to scenario-based stochastic model predictive control, Journal of Process Control, Vol. 75, pp. 24–39, 2019.

Westermann, P. et al.: Unsupervised learning of energy signatures to identify the heating system and building type using smart meter data, Applied Energy, Vol. 264, 114715, pp. 1–14, 2020.

Liu, T. et al.: A novel deep reinforcement learning based methodology for short-term HVAC system energy consumption prediction, International Journal of Refrigeration, Vol. 107, pp. 39–51, 2019.

Wang, Z. and Hong, T.: Reinforcement learning for building controls: The opportunities and challenges, Applied Energy, Vol. 269, 115036, pp. 1–18, 2020.

Ahmad, M. W. et al.: Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption, Energy and Buildings, Vol. 147, pp. 77–89, 2017.

Touzani, S. et al.: Gradient boosting machine for modeling the energy consumption of commercial buildings, Energy and Buildings, Vol. 158, pp. 1533–1543, 2018.

Seyedzadeh, S. et al.: Data driven model improved by multi-objective optimisation for prediction of building energy loads, Automation in Construction, Vol. 116, 103188, pp. 1–12, 2020.

Wang, Z. et al.: Building thermal load prediction through shallow machine learning and deep learning, Applied Energy, Vol. 263, 114683, pp. 1–14, 2020.

Ghahramani, A. et al.: Learning occupants’ workplace interactions from wearable and stationary ambient sensing systems, Applied Energy, Vol. 230, pp. 42–51, 2018.

****: Rulebook on energy efficiency of buildings, Official Gazette of the Republic of Serbia, Vol. 61, 2011. (In Serbian)

Stojiljković, M. M. et al.: Cost-optimal energy retrofit for Serbian residential buildings connected to district heating systems, Thermal Science, Vol. 23, Suppl. 5, pp. 1707–1717, 2019.



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ISSN   0354-804X (Print)

ISSN   2406-0534 (Online)