A Machine Learned Approach to Identify the Anomalies in Load Pattern of Pakistan

Adeel Shams

Abstract


The energy crisis of Pakistan is worsening day by day for different explicit and implicit reasons. Load pattern and its use in the studies of power system is a vital area. Here it is used for the identification and analysis of anomalies in the load pattern of Pakistan while using Support Vector Regressor Machine as a Machine Learning tool. The training phase has been provided with retrospective data of electrical load, temperature, and relative humidity so as to predict the future electrical load in the testing phase, based on the then data of temperature and relative humidity. Based on temperature, three groups of electrical load have been opted based on particle swarm optimization clustering namely moderate, cold and hot. The difference curve between the actual load and predicted load illustrated various anomalies in all of the three clusters. The high numbers of anomalies were found in the hot cluster whilst confirming the dependence of load pattern upon the weather parameters. The analysis of the difference curve between the patterns portrays to be deformed enormously. The analysis of the anomalies has also being carried out in terms of contextualized parameters.


Keywords


Electric Load Pattern, Machine Learning, SVM Regressor, Cold Cluster, Hot Cluster, Moderate Cluster

Full Text:

PDF

References


N. Alter and S. H. Syed,” An empirical analysis of electricity demand in Pakistan,” International Journal of Energy Economics and Policy, vol. 1, p. 116, 2011.

GOP. National power policy 2013. Government of Pakistan; 2013

PPIB, Hydro Power Resource of Pakistan, Private Power and In-frastructure Board, Islamabad, Pakistan, 2018 [online] Available at:http://www.ppib.gov.pk/ [Accessed 21 April, 2019].

Mirjat, N. H., Uqaili, M. A., Harijan, K., Valasai, G. D., Shaikh, F., and Waris, M. (2017). A review of energy and power planning and policies of Pakistan. Renewable and Sustainable Energy Reviews, 79, 110-127.

J. Navani, N. Sharma, and S. Sapra, ”Technical and non-technical losses in power system and its economic consequence in Indian economy,” International Journal of Electronics and Computer Science Engineering, vol. 1, pp. 757-761, 2012.

V. Figueiredo, F. Rodrigues, Z. Vale, and J. B. Gouveia, ”An electric energy consumer characterization framework based on data mining techniques,” IEEE Transactions on Power Systems, vol. 20, pp. 596-602, 2005.

S. Allera and A. Horsburgh, ”Load profiling for the energy trading and settlements in the UK electricity markets,” in Proc. DistribuTECH Europe DA/DSM Conference, 1998, pp. 27-29.

G. Chicco, R. Napoli, P. Postolache, M. Scutariu, and C. Toader, Customer characterization options for improving the tariff offer, IEEE Power Eng. Rev., vol. 22, no. 11, p. 60, Nov. 2002.

I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical

machine learning tools and techniques: Morgan Kaufmann, 2016.

H. K. Alfares and M. Nazeeruddin, ”Electric load forecasting: literature survey and classification of methods,” International Journal of Systems Science, vol. 33, pp. 23-34, 2002.

P. K. Sarangi, N. Singh, R. Chauhan, and R. Singh, ”Short term load forecasting using artificial neural network: a comparison with genetic algorithm implementation,” Journal of Engineering and Applied Sciences, vol. 4, pp. 88-93, 2009.

G. Chicco and I.-S. Ilie, ”Support vector clustering of electrical load pattern data,” IEEE Transactions on Power Systems, vol. 24, pp. 1619-1628, 2009.

A. Nizar, Z. Dong, and Y. Wang, ”Power utility nontechnical loss analysis with extreme learning machine method,” IEEE Transactions on Power Systems, vol. 23, pp. 946-955, 2008.

J. Nagi, K. Yap, S. Tiong, S. Ahmed, and A. Mohammad, ”Detection of abnormalities and electricity theft using genetic support vector machines,” in TENCON 2008-2008 IEEE Region 10 Conference, 2008, pp. 1-6.

H. Shi-wang, S. Feng, and H. Wang, ”Intelligent Process Abnormal Pat-terns Recognition and Diagnosis Based on Fuzzy Logic,” Computational Intelligence and Neuroscience: CIN, vol. 2016, 2016.

A. ul Asar and U. Amjad, ”Application of Data Mining Techniques in the Identification of Key Weather Parameters Affecting Electric Load Patterns,” in DMIN, 2008, pp. 355-361.

T. Hong, P. Wang, and L. White, ”Weather station selection for electric load forecasting,” International Journal of Forecasting, vol. 31, pp. 286-295, 2015.

A. P. Douglas, A. M. Breipohl, F. N. Lee, and R. Adapa, ”The impacts of temperature forecast uncertainty on Bayesian load forecasting,” IEEE Transactions on Power Systems, vol. 13, pp. 1507-1513, 1998.

J. Xie, Y. Chen, T. Hong, and T. D. Laing, ”Relative humidity for load forecasting models,” IEEE Transactions on Smart Grid, 2016.

Khan, K., Attique, M., Syed, I., Sarwar, G., Irfan, M.A. and Khan, R.U., 2019. A unified framework for head pose, age and gender classification through end-to-end face segmentation. Entropy, 21(7), p.647.

Khan, K., Attique, M., Syed, I. and Gul, A., 2019. Automatic gender classification through face segmentation. Symmetry, 11(6), p.770.




DOI: http://dx.doi.org/10.36785/jaes.111488

Creative Commons License
Journal of Applied and Emerging Sciences by BUITEMS is licensed under a Creative Commons Attribution 4.0 International License.
Based on a work at www.buitms.edu.pk.
Permissions beyond the scope of this license may be available at http://journal.buitms.edu.pk/j/index.php/bj

Contacts | Feedback
© 2002-2014 BUITEMS