Lazar Sladojevic, Aleksandar Janjic

DOI Number
First page
Last page


This paper represents an approach for the estimation and forecast of losses in a distribution power grid from data which are normally collected by the grid operator. The proposed approach utilizes the least squares optimization method in order to calculate the coefficients needed for estimation of losses. Besides optimization, a machine learning technique is introduced for clustering of coefficients into several seasons. The amount of data used in calculations is very large due to the fact that electrical energy injected in distribution grid is measured every fifteen minutes. Therefore, this approach is classified as the big data analysis. The used data sets are available in the Serbian distribution grid operator’s report for the year 2017. Obtained results are fairly accurate and can be used for losses classification as well as future losses estimation.


grid losses, least squares optimization, big data, clustering

Full Text:



L. Sladojević, A. Janjić, M. Ćirković „Calculation of Losses in the Distribution Grid Based on Big Data“, In Proceedings of the 4th Virtual International Conference on Science, Technology and Management in Energy, eNergetics 2018, October 25-26, 2018, pp. 19-22

K. Zhou, C. Fu and S. Yang, “Big data driven smart energy management: From big data to big insights”, Renewable and Sustainable Energy Reviews, vol. 56, pp. 215–225, 2016.

O. Ardakanian, N. Koochakzadeh, R. P. Singh, L. Golab, and S. Keshav, “Computing Electricity Consumption Profiles from Household Smart Meter Data,” In Proceedings of the EDBT Workshop on Energy Data Management, 2014, pp. 140–147.

H. Jiang, J. Zhang, W. Gao, and Z. Wu, “Fault detection, identification, and location in smart grid based on data-driven computational methods,” IEEE Transaction on Smart Grid, vol. 5, no. 6, pp. 2947–2956, 2015.

D. N. Nikovski, Z. Wang, A. Esenther, H. Sun, K. Sugiura, T. Muso and K. Tsuru, “Smart meter data analysis for power theft detection”, In Proceedings of the Int. Workshop Machine Learning and Data Mining in Pattern Recognition, ser. LNCS, vol. 7988. Springer, 2013, pp. 379–389.

P. Nallagownden and T. P. Hong, “Development of a new loss prediction method in a deregulated power market using proportional sharing,” In Proceddings of Power Engineering and Optimization Conference (PEOCO), 2011 5th International. Shah Alam, Selangor, Malaysia: IEEE, June 2011, pp. 48–53.

A. Petrušić, A. Janjić, “Economic Regulation of Electric Power Distribution Network”, In Proceedings of 2nd Virtual International Conference on Science, Technology and Management in Energy, Niš, Serbia, 2016, pp. 25-32

W. D. Liu, T. J. Zeng, T. Jun, L. L. Shang Guan, and B. J. Li, “Research on Electric Power with Development and Application of Line Loss Rate Forecasting Software Based on MLRM-GM,” Advanced Materials Research, vol. 977, pp. 182–185, June 2014.

K. Li, Z. Q. Sun, and M. Wang, “Theoretical Line Loss Calculation of Distribution Network Considering Wind Turbine Power Constraint,” Advanced Materials Research, vol. 986-987, pp. 630–634, July 2014.

Q. Ding and A. Abur, “Transmission Loss Allocation Based on a New Quadratic Loss Expression,” IEEE Transactions on Power Systems, vol. 21, pp. 1227–1233, August 2006.

G. Gross and S. Tao, “A Physical-Flow-Based Approach to Allocating Transmission Losses in a Transaction Framework,” IEEE Transactions on Power Systems, vol. 15, pp. 631–637, May 2000.

Elmitwally, A. Eladl, and S. M. Abdelkader, “Efficient algorithm for transmission system energy loss allocation considering multilateral contracts and load variation,” IET Generation, Transmission & Distribution, vol. 9, pp. 2653–2663, August 2015.

L. Tian, Q. Q. Wang, and A. Z. Cao, “Research on SVM Line Loss Rate Prediction Based on Heuristic Algorithm,” Applied Mechanics and Materials, vol. 291-294, pp. 2164–2168, February 2013.

Y. Ren, X. G. Zhang, and X. C. Huang, “Study on the Prediction of Line Loss Rate Based on the Improved RBF Neural Network,” Advanced Materials Research, vol. 915-916, pp. 1292–1295, April 2014.

P. S. Nagendra Rao and R. Deekshit Energy Loss Estimation in Distribution Feeders IEEE Transactions on Power Delivery, vol. 21, no. 3, July 2006.

M.W. Gustafson, J. S. Baylor, and S. S. Mulnix, “Equivalent hours loss factor revisited,” IEEE Trans. Power Syst., vol. 3, no. 4, pp. 1502–1507, Nov. 1988.

M. W. Gustafson, “Demand, energy and marginal electric system losses,” IEEE Trans. Power App. Syst., vol. PAS-102, no. 9, pp. 3189–3195, Sep. 1983.

W. Gustafson and J. S. Baylor, “Approximating the system losses equation,” IEEE Trans. Power Syst., vol. 4, no. 3, pp. 850–855, Aug. 1989

M. Järvinen, “Developing network loss forecasting for Distribution System Operator”, Master of Science Thesis, Tampere University of Technology, 2013, accessed on 26.2.2019.

J. Löfberg, “YALMIP: A Toolbox for Modeling and Optimization in MATLAB”, In Proceedings of the CACSD Conference, Taipei, Taiwan, 2–4 September 2004.

Waltz, R. A., J. L. Morales, J. Nocedal, and D. Orban, “An interior algorithm for nonlinear optimization that combines line search and trust region steps,” Mathematical Programming, vol. 107, no. 3, pp. 391–408, 2006.

G. James, D. Witten, T. Hastie, R. Tibshirani, ” An Introduction to Statistical Learning with Applications in R”, Springer Texts in Statistics, Springer Science + Business Media New York, 2013 (Corrected at 6th printing 2015).


  • There are currently no refbacks.

ISSN: 0353-3670 (Print)

ISSN: 2217-5997 (Online)

COBISS.SR-ID 12826626