Data Driven Building Electricity Consumption Model Using Support Vector Regression


  • FX Nugroho Soelami Institut Teknologi Bandung
  • Putu Handre Kertha Utama Institut Teknologi Bandung
  • Irsyad Nashirul Haq Institut Teknologi Bandung
  • Justin Pradipta Institut Teknologi Bandung
  • Edi Leksono Institut Teknologi Bandung
  • Meditya Wasesa Institut Teknologi Bandung



building electricity consumption prediction, consumption patterns, data driven modeling, historical database, support vector regression


Every building has certain electricity consumption patterns that depend on its usage. Building electricity budget planning requires a consumption forecast to determine the baseline electricity load and to support energy management decisions. In this study, an algorithm to model building electricity consumption was developed. The algorithm is based on the support vector regression (SVR) method. Data of electricity consumption from the past five years from a selected building object in ITB campus were used. The dataset unexpectedly exhibited a large number of anomalous points. Therefore, a tolerance limit of hourly average energy consumption was defined to obtain good quality training data. Various tolerance limits were investigated, that is 15% (Type 1), 30% (Type 2), and 0% (Type 0). The optimal model was selected based on the criteria of mean absolute percentage error (MAPE) < 20% and root mean square error (RMSE) < 10 kWh. Type 1 data was selected based on its performance compared to the other two. In a real implementation, the model yielded a MAPE value of 14.79% and an RMSE value of 7.48 kWh when predicting weekly electricity consumption. Therefore, the Type 1 data-based model could satisfactorily forecast building electricity consumption.


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Author Biographies

FX Nugroho Soelami, Institut Teknologi Bandung

Engineering Physics - ITB

Putu Handre Kertha Utama, Institut Teknologi Bandung

Engineering Physics - ITB

Irsyad Nashirul Haq, Institut Teknologi Bandung

Engineering Physics - ITB

Justin Pradipta, Institut Teknologi Bandung

Engineering Physics - ITB

Edi Leksono, Institut Teknologi Bandung

Engineering Physics - ITB

Meditya Wasesa, Institut Teknologi Bandung

School of Business and Management - ITB


Yoo S.G. & Myriam, H.A., Predicting Residential Electricity Consumption Using Neural Networks: A Case Study, J. Phys. Conf. Ser., 1072(1), 012005, 2018. DOI: 10.1088/1742-6596/1072/1/012005.

Zhong, H., Wang, J., Jia, J., Mu, Y. & Lv, S., Vector Field-based Support Vector Regression for Building Energy Consumption Prediction, APEN Appl. Energy, 242, pp. 403-414, 2019.

Wang, Z. & Srinivasan, R.S., A Review of Artificial Intelligence Based Building Energy Use Prediction: Contrasting the Capabilities of Single and Ensemble Prediction Models, Renew. Sustain. Energy Rev., 75, pp. 796-808, 2017.

Ma, Z., Ye, C. & Ma, W., Support Vector Regression for Predicting Building Energy Consumption in Southern China, Energy Procedia, 158, pp. 3433-3438, 2019. DOI: 10.1016/j.egypro.2019.01.931.

Biswas, M.A.R., Robinson, M.D. & Fumo, N., Prediction of Residential Building Energy Consumption: A Neural Network Approach, EGY Energy, 117, pp. 84-92, 2016.

Chen, Y., Short-Term Electrical Load Forecasting Using the Support Vector Regression (SVR) Model to Calculate the Demand Response Baseline for Office Buildings, Appl. Energy, 195, pp. 659-670, 2017. DOI: 10.1016/j.apenergy.2017.03.034.

Chammas, M., Makhoul, A. & Demerjian, J., An Efficient Data Model for Energy Prediction Using Wireless Sensors, Comput. Electr. Eng., 76, pp. 249-257, 2019. DOI: 10.1016/j.compeleceng.2019.04.002.

Fayaz, M., Shah, H., Aseere, A., Mashwani, W. & Shah, A., A Framework for Prediction of Household Energy Consumption Using Feed Forward Back Propagation Neural Network, Technologies, 7(2), 30, 2019. DOI: 10.3390/technologies7020030.

Zhang, X.M., Grolinger, K., Capretz, M.A.M. & Seewald, L., Forecasting Residential Energy Consumption: Single Household Perspective, Proc. 17th IEEE Int. Conf. Mach. Learn. Appl. ICMLA 2018, pp. 110-117, 2019. DOI: 10.1109/ICMLA.2018.00024.

Li, Z., Friedrich, D. & Harrison, G.P., Demand Forecasting for a Mixed-use Building Using Agent-Schedule Information with a Data-driven Model, Energies, 13(4), 780, 2020. DOI: 10.3390/en13040780.

Guo, Y., Machine Learning-based Thermal Response Time Ahead Energy Demand Prediction for Building Heating Systems, Appl. Energy, 221(March), pp. 16-27, 2018. DOI: 10.1016/j.apenergy.2018.03.125.

Burger, E.M. & Moura, S.J., Building Electricity Load Forecasting via Stacking Ensemble Learning Method with Moving Horizon Optimization, UC Berkeley: Energy, Controls, and Applications Lab., 2015. Unpublished. Available:

Zhang, F., Deb, C., Lee, S.E., Yang, J. & Shah, K.W., Time Series Forecasting for Building Energy Consumption Using Weighted Support Vector Regression with Differential Evolution Optimization Technique, ENB Energy Build., 126, pp. 94-103, 2016.

Koschwitz, D., Frisch, J. & Treeck, C. van, 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(PA), pp. 134-142, 2018.

Ceperic, E., Ceperic, V. & Baric, A., A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines, IEEE Trans. Power Syst., 28(4), pp. 4356-4364, 2013. DOI: 10.1109/TPWRS.2013.2269803.

Haq, I.N., Saputra, R.H., Edison, F., Kurniadi, D., Leksono, E. & Yuliarto, B., State of Charge (SoC) Estimation of LiFePO4 Battery Module Using Support Vector Regression, Proc. Jt. Int. Conf. Electr. Veh. Technol. Ind. Mech. Electr. Chem. Eng. ICEVT 2015 IMECE 2015, pp. 16-21, 2015. DOI: 10.1109/ICEVTIMECE.2015.7496640.

Xiao, T., Ren, D., Lei, S., Zhang, J. & Liu, X., Based on Grid-search and PSO Parameter Optimization for Support Vector Machine, Proc. World Congr. Intell. Control Autom., 2015. DOI: 10.1109/WCICA.2014. 7052946.

Hong, W.C., Ed., Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting, Basel, Switzerland, MDPI, 2018.

Kabalci, Y., A Survey on Smart Metering and Smart Grid Communication, Renew. Sustain. Energy Rev., 57, pp. 302-318, 2016. DOI: 10.1016/j.rser. 2015.12.114.

Haq, I.N., Kurniadi, D., Leksono, E. & Yuliarto, B., Performance Analysis of Energy Storage in Smart Microgrid Based on Historical Data of Individual Battery Temperature and Voltage Changes, J. Eng. Technol. Sci., 51(2), pp. 149-169, 2019. DOI: 10.5614/j.eng.technol.sci.2019.51. 2.1.

Friansa, K., Haq, I.N., Santi, B.M., Kurniadi, D., Leksono, E. & Yuliarto, B., Development of Battery Monitoring System in Smart Microgrid Based on Internet of Things (IoT), Procedia Eng., 170, pp. 482-487, 2017. DOI: 10.1016/j.proeng.2017.03.077.

Cecati, C., Kolbusz, J., Rycki, P., Siano, P. & Wilamowski, B.M., A Novel RBF Training Algorithm for Short-term Electric Load Forecasting and Comparative Studies, IEEE Trans. Ind. Electron., 62(10), pp. 6519-6529, 2015. DOI: 10.1109/TIE.2015.2424399.




How to Cite

Soelami, F. N., Kertha Utama, P. H., Haq, I. N., Pradipta, J., Leksono, E., & Wasesa, M. (2021). Data Driven Building Electricity Consumption Model Using Support Vector Regression. Journal of Engineering and Technological Sciences, 53(3), 210313.