Support Vector Machine to Predict Electricity Consumption in the Energy Management Laboratory

  • Azam Zamhuri Fuadi Universitas Telkom
  • Irsyad Nashirul Haq Institut Teknologi Bandung
  • Edi Leksono Institut Teknologi Bandung
Keywords: Prediction, Electrical Consumption, profile load, machine learning, SUpport vector machine


Predicted electricity consumption is needed to perform energy management. Electricity consumption prediction is also very important in the development of intelligent power grids and advanced electrification network information. we implement a Support Vector Machine (SVM) to predict electrical loads and results compared to measurable electrical loads. Laboratory electrical loads have their own characteristics when compared to residential, commercial, or industrial, we use electrical load data in energy management laboratories to be used to be predicted. C and Gamma as searchable parameters use GridSearchCV to get optimal SVM input parameters. Our prediction data is compared to measurement data and is searched for accuracy based on RMSE (Root Square Mean Error), MAE (Mean Absolute Error) and MSE (Mean Squared Error) values. Based on this we get the optimal parameter values C 1e6 and Gamma 2.97e-07, with the result RSME (Root Square Mean Error) ; 0.37, MAE (meaning absolute error); 0.21 and MSE (Mean Squared Error); 0.14.


Download data is not yet available.


Yuan, R., Li, Z., Guan, X. et al. An SVM-based machine learning method for accurate internet traffic classification. Inf Syst Front 12, 149–156 (2010).

Gao, J. B., Gunn, S. R., & Harris, C. J. (2003). SVM regression through variational methods and its sequential implementation. Neurocomputing, 55(1-2), 151–167. doi:10.1016/s0925-2312(03)00365-5

Moura, M. das C., Zio, E., Lins, I. D., & Droguett, E. (2011). Failure and reliability prediction by support vector machines regression of time series data. Reliability Engineering & System Safety, 96(11), 1527–1534. doi:10.1016/j.ress.2011.06.006.

Gao, J., Gunn, S., Harris, C. et al. A Probabilistic Framework for SVM Regression and Error Bar Estimation. Machine Learning 46, 71–89 (2002).

Mocanu, E., Nguyen, P.H., Madeleine Gibescu, M., Kling, W.L., 2016. Deep learning for estimating builing energy consumption. Sustainale Energy, Grid and Networks, 6, pp.91-99.

Rahman, A., Srikumar, V., Smith, A.D., 2018. Predicting electrical consumption for commercial and residential buildings using deep recurrent neural networks. Applied Energy, 212, pp.372-385.

Wu, C.H., Ho, J.M., Lee, D-T., 2004. Travel-time prediction with support vector regression. IEEE Transactions on Intelligent Transportation Systems, 5.4,pp.276-281

Salcedo-Sanz, Sancho, et al., 2011. Short term wind speed prediction based on evolutionary support vector regression algorithms. Expert Systems with Applications, 38.4,pp.4052-4057.

Costa, A., Keane, M.M., Torrens, J.I., Corry, E., 2013. Building operation and energy performance: Monitoring, analysis and optimisation toolkit. Applied Energy, 101,pp.310–316.

Simoes, M., Roche, R., Kyriakides, E., Suryanarayanan, S., Blunier, B., McBee, K., Nguyen, P.H., Ribeiro, P.F., Miraoui, A., 2012. A comparison of smart grid technologies and progresses in Europe and the US. IEEE Transactions on Industry Applications, 48(4), pp.1154–1162.

Mocanu, E., Nguyen, P.H., Gibescu M., Kling, W.L.,2016. Deep learning for estimating building energy consumption. Sustain Energy Grids Networks,6, 2016;6:91–9.

Haiqin Yang, Laiwan Chan, and Irwin King. 2002. Support Vector Machine Regression for Volatile Stock Market Prediction. In Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning (IDEAL '02). Springer-Verlag, Berlin, Heidelberg, 391–396.

Parbat, Debanjan, & Chakraborty, 2020. A python based support vector regression model for prediction of COVID19 cases in India. Chaos, Solitons & Fractals, 138, (2020): 109942.

Paniagua-Tineo, A., et al., 2011. Prediction of daily maximum temperature using a support vector regression algorithm. Renewable Energy, 36,11 (2011): 3054-3060.

Claesen, Marc, et al., 2014. Fast prediction with SVM models containing RBF kernels. arXiv preprint arXiv:1403.0736 (2014).

Abakar, Khalid & Yu, Chongwen., 2014. Performance of SVM based on PUK kernel in comparison to SVM based on RBF kernel in prediction of yarn tenacity. Indian Journal of Fibre and Textile Research, 39,55-59.

Huang, Min-Wei, et al., 2017. SVM and SVM ensembles in breast cancer prediction. PloS one, 12,1 (2017): e0161501.

Saputra, L.H., 2017. Design and Implementation of Thermal Management System of LiFeMnPO4 with Optimization Using Support Vector Machine. Postgraduate Theses, Engineering Physics ITB.

Chang, Chih-Chung, & Chih-Jen L., 2011. LIBSVM: a library for support vector machines. ACM transactions on intelligent systems and technology (TIST), 2,3 (2011): 1-27.

How to Cite
Zamhuri Fuadi, A., Irsyad Nashirul Haq, & Edi Leksono. (2021). Support Vector Machine to Predict Electricity Consumption in the Energy Management Laboratory. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(3), 466 - 473.
Information Technology Articles