Prediction of Heating and Cooling Load to improve Energy Efficiency of Buildings Using Machine Learning Techniques


Srihari J,Santhi B,



Energy efficiency,Heating Load,Cooling Load,Machine Learning,


Global warming has been a severe threat to humanityand greenhouse gases emitted from power plants is one of the major causes of global warming. In this paper, we use machine learning to incorporate energy efficiency techniques to buildings by predicting the Heating and Cooling Load using eight input features.Heating load is the amount of heat per unit time that a building needs to maintain the temperature at an established level whereas Cooling load is the amount of heat per unit time that must be removed. Heating, cooling, and ventilation systems are used to handle heating and cooling load. We train four regression (linear regression, Lasso, Ridge, and Elastic-Net) and three gradient boosting models (GBM, XGBoost, and LightGBM) and test them to compare their performance using 768 rows of data of residential buildings. We observe that the gradient boosting models perform significantly better than the standard regression models for both Heating Load and Cooling Load. XGBoost achieves the highest R-squared score of 0.99 for Heating Load and 0.99 for Cooling Load. From the results of this study, we conclude that machine learning techniques can predict Heating Load and Cooling Load with high accuracy. The obtained Heating load and cooling load values can be used to install efficient heating, cooling and ventilation systems and thus reduce both energy consumption and money.


I.Al Fardan, A. S., Al Gahtani, K. S., and Asif, M. (2017). Demand side management solution through new tariff structure to minimize excessive load growth and improve system load factor by improving commercial buildings energy performance in Saudi Arabia. 2017 5th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2017, pages 302–308.

II.Bizjak, M., Zalik, B.,ˇ Stumberger, G., and Lukaˇc, N. (2018). Estimation andˇ optimisation of buildings’ thermal load using LiDAR data. Building and Environment, 128:12–21.

III.Borgstein, E. H., Lamberts, R., and Hensen, J. L. (2018). Mapping failures in energy and environmental performance of buildings. Energy and Buildings, 158:476–485.

IV.Caputo, P., Costa, G., and Ferrari, S. (2013). A supporting method for defining energy strategies in the building sector at urban scale. Energy Policy, 55:261 –270. Special section: Long Run Transitions to Sustainable Economic Structures in the European Union and Beyond.V.Cetin, K. S., Tabares-Velasco, P. C., and Novoselac, A. (2014). Appliance daily energy use in new residential buildings: Use profiles and variation in time-ofuse. Energy and Buildings, 84:716–726.

VI.Chen, T. and Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System.VII.Cheng, V. and Steemers, K. (2011). Modelling domestic energy consumption at district scale: A tool to support national and local energy policies. Environmental Modelling Software, 26(10):1186 –1198.VIII.Dai, C., Zhang, H., Arens, E., and Lian, Z. (2017). Machine learning approaches to predict thermal demands using skin temperatures: Steady-state conditions. Building and Environment, 114:1–10.IX.Deng, H., Fannon, D., and Eckelman, M. J. (2018). Predictive modeling for US commercial building energy use: A comparison of existing statistical and machine learning algorithms using CBECS microdata. Energy and Buildings, 163:34–43.X.Dheeru, D. and KarraTaniskidou, E. (2017). UCI machine learning repository.XI.Flett, G. and Kelly, N. (2017). A disaggregated, probabilistic, high resolution method for assessment of domestic occupancy and electrical demand. Energy and Buildings, 140:171–187.XII.Fonseca, J. A.and Schlueter, A. (2015). Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts. Applied Energy, 142:247 –265.XIII.Guo, Y., Li, G., Chen, H., Wang, J., and Huang, Y. (2017). A thermal response time ahead energy demand prediction strategy for building heating system using machine learning methods. Energy Procedia, 142:1003–1008.XIV.Gupta, N. and Shet, H. N. (2016). Analysis of Measures to Improve EnergyXV.Performance of a Commercial Building by Energy Modeling.2016 Online International Conference on Green Engineering and Technologies (IC-GET) Analysis, pages 1–4.XVI.Hamid, M. F. A., Ramli, N. A., and Syawal Nik Mohd Kamal, N. M. F. (2017). An analysis of energy performance of a commercial building using energy modeling. In 2017 IEEE Conference on Energy Conversion (CENCON), pages 105–110. IEEE.XVII.Holmegaard, E., Johansen, A., and Kjærgaard, M. B. (2016). Towards a metadata discovery, maintenance and validation process to support applications that improve the energy performance of buildings. 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016.XVIII.Jaffal, I. and Inard, C. (2017). A metamodel for building energy performance. Energy and Buildings, 151:501–510.

XIX.Jeong, Y.-k., Kim, T., Nam, H.-S., and Lee, I.-w. (2016). Implementation of energy performance assessment system for existing building. 2016 International Conference on Information and Communication Technology Convergence (ICTC), (20142010102370):393–395.XX.Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 3146–3154. Curran Associates, Inc.XXI.Kim, J., Zhou, Y., Schiavon, S., Raftery, P., and Brager, G. (2018). Personal comfort models: Predicting individuals’ thermal preferenceusing occupant heating and cooling behavior and machine learning. Building and Environment, 129:96–106.XXII.Konis, K. and Annavaram, M. (2017). The Occupant Mobile Gateway: A participatory sensing and machine-learning approach for occupant-aware energy management. Building and Environment, 118:1–13.XXIII.Kwok, S. S. K., Yuen, R. K. K., and Lee, E. W. M. (2011). An intelligent approach to assessing the effect of building occupancy on building cooling load prediction. Building and Environment, 46(8):1681–1690.XXIV.Onose, B.-a. (2016). Control optimization for increasing energy performance of existing buildings. 2016 Eleventh International Conference on Ecological Vehicles and Renewable Energies (EVER), pages 1–4.XXV.Parise, G., Martirano, L., and Parise, L. (2014). Energy performance of buildings: An useful procedure to estimate the impact of the lighting control systems. Conference Record -Industrial and Commercial Power Systems Technical Conference, pages 1–7.XXVI.Robinson, C., Dilkina, B., Hubbs, J., Zhang, W., Guhathakurta, S., Brown, M. A., and Pendyala, R. M. (2017). Machine learning approaches for estimating commercial building energy consumption. Applied Energy, 208(May):889–904.XXVII.Shimoda, Y., Fujii, T., Morikawa, T., and Mizuno, M. (2004). Residential enduse energysimulation at city scale. Building and Environment, 39(8):959 –967. Building Simulation for Better Building Design.XXVIII.Song, M., Niu, F., Mao, N., Hu, Y., and Deng, S. (2018). Review on building energy performance improvement using phase change materials. Energy and Buildings, 158:776–793.

XXIX.Talebi, B., Haghighat, F., and Mirzaei, P. A. (2017). Simplified model to predict the thermal demand profile of districts. Energy and Buildings, 145:213 –225.XXX.Talebi, B., Haghighat, F., Tuohy, P., and Mirzaei, P. A. (2018). Validation of a community district energy system model using field measured data. Energy, 144:694 –706.XXXI.Touzani, S., Granderson, J., and Fernandes, S. (2018). Gradient boosting machine for modeling the energy consumption of commercial buildings. Energy and Buildings, 158:1533–1543.XXXII.Tsanas, A. and Xifara, A. (2012). Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy and Buildings, 49:560–567.XXXIII.Tuominen, P., Holopainen, R.,Eskola, L., Jokisalo, J., and Airaksinen, M. (2014). Calculation method and tool for assessing energy consumption in the building stock. Building and Environment, 75:153 –160.XXXIV.Vujoˇsevi ́c, M. and Krsti ́c-Furundˇzi ́c, A. (2017). The influence of atrium on energy performance of hotel building. Energy and Buildings, 156:140–150.XXXV.Wang, Z., Wang, Y., and Srinivasan, R. S. (2018). A novel ensemble learning approach to support building energy use prediction. Energy and Buildings, 159:109–122.

Author(s): Srihari J, Santhi B View Download