PREDICTING PRIMARY ENERGY SAVINGS OF BUILDING RETROFIT MEASURES WITH DECISION-TREE-BASED ENSEMBLE METHODS
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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.
DOI: https://doi.org/10.22190/FUWLEP2003151S
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