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	An intelligent shell game optimization based energy consumption analytics model for smart metering data</div>

Ramalingam Saravanan, Arulnanthisivam Swaminathan, Sankaralingam Balaji

2023 , VOLUME 23, NUMBER 2 ( March-April )

ISSN 2226-1494 (print), ISSN 2500-0373 (online)

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Nikiforov
Vladimir O.
D.Sc., Prof.

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doi: 10.17586/2226-1494-2023-23-2-374-381

An intelligent shell game optimization based energy consumption analytics model for smart metering data

R. Saravanan, A. Swaminathan, S. Balaji

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Saravanan R., Swaminathan A., Balaji S. An intelligent shell game optimization based energy consumption analytics model for smart metering data. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 2, pp. 374–381. doi: 10.17586/2226-1494-2023-23-2-374-381

Abstract

Smart metering is a hot research topic and has gained significant attention since the electromechanical metering is not reliable and requires more energy and time. All the existing methods are focused only on how to deal with data rather than how to do efficiently. Prediction of electricity consumption is essential to gain intelligence to the smart gird. Precise electricity prediction allows a service provided in resource planning and also controlling actions for the demand and supply balancing. The users are beneficial from the smart metering solution by effective interpretation of their energy utilization, and labelling them to efficiently handle the utilization cost. With this motivation, the paper presents intelligent energy consumption analytics using smart metering data (ECA-SMD) model to determine the utilization of energy. The presented ECA-SMD model involves three major processes namely data pre-processing, feature extraction, classification, and parameter optimization. The presented ECA-SMD model uses Extreme Learning Machine (ELM) based classification to determine the optimum class labels. Besides, shell game optimization (SGO) algorithm is applied for tuning the parameters involved in the ELM and boosts the classification efficiency. The efficacy of the ECA-SMD model is validated using an extensive set of smart metering data and the results are investigated based on accuracy and mean square error (MSE). The proposed model exhibited supremacy with the maximum accuracy of 65.917 % and minimum MSE of 0.096.

Keywords: electricity consumption, predictive model, data analytics, smart metering, machine learning

References

Mehdipour Pirbazari A., Farmanbar M., Chakravorty A., Rong C. Short-term load forecasting using smart meter data: A generalization analysis. Processes, 2020, vol. 8, no. 4, pp. 484. https://doi.org/10.3390/pr8040484
Uthayakumar J., Metawa N., Shankar K., Lakshmanaprabu S.K. Intelligent hybrid model for financial crisis prediction using machine learning techniques. Information Systems and e-Business Management,2020, no. 4, pp. 617–645. https://doi.org/10.1007/s10257-018-0388-9
Wang Y.,Chen Q.,Hong T.,Kang C. Review of smart meter data analytics: applications, methodologies, and challenges. IEEE Transactions on Smart Grid,2019,vol. 10,no. 3, pp. 3125–3148. https://doi.org/10.1109/tsg.2018.2818167
Farmanbar M.,Parham K.,Arild Ø.,Rong C. A widespread review of smart grids towards smart cities. Energies,2019,vol. 12,no. 23, pp. 4484. https://doi.org/10.3390/en12234484
Rajakumar R., Sivanandakumar D., Uthayakumar J., Vengattaraman T., Dinesh K. Optimal parameter tuning for PID controller using accelerated grey wolf optimisation in smart sensor environments. Electronic Government, an International Journal, 2020, vol. 16, no. 1-2, pp. 170–189. https://doi.org/10.1504/eg.2020.105237
Ryu S., Noh J., Kim H. Deep neural network based demand side short term load forecasting. Energies, 2016, vol. 10, no. 1, pp. 3. https://doi.org/10.3390/en10010003
Mocanu E.,Nguyen P.H.,Gibescu M.,Kling W.L. Comparison of machine learning methods for estimating energy consumption in buildings.Proc. of the 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2014,pp. 1–6. https://doi.org/10.1109/pmaps.2014.6960635
Pirbazari A.M.,Chakravorty A.,Rong C. Evaluating feature selection methods for short-term load forecasting. Proc. of the 2019 IEEE International Conference on Big Data and Smart Computing (BigComp), 2019,pp. 1–8. https://doi.org/10.1109/bigcomp.2019.8679188
Zhao H.,Magoulès F. A review on the prediction of building energy consumption. Renewable and Sustainable Energy Reviews, 2012, vol. 16, no. 6, pp. 3586–3592. https://doi.org/10.1016/j.rser.2012.02.049
Pérez-Lombard L.,Ortiz J.,Pout C. A review on buildings energy consumption information. Energy and Buildings, 2008,vol. 40, no. 3, pp. 394–398. https://doi.org/10.1016/j.enbuild.2007.03.007
Hyndman R., Koehler A., Ord K., Snyder R. Forecasting with Exponential Smoothing. Berlin/Heidelberg, Germany, Springer, 2008, 362 p. https://doi.org/10.1007/978-3-540-71918-2
Ahmad A.S.,Hassan M.Y.,Abdullah M.P.,Rahman H.A.,Hussin F.,Abdullah H.,Saidur R. A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renewable and Sustainable Energy Reviews, 2014,vol. 33, pp. 102–109. https://doi.org/10.1016/j.rser.2014.01.069
Khashei M., Bijari M. An artificial neural network (p,d,q) model for timeseries forecasting. Expert Systems with Applications, 2010, vol. 37, no. 1, pp. 479–489. https://doi.org/10.1016/j.eswa.2009.05.044
Murugan B.S., Elhoseny M., Shankar K., Uthayakumar J. Region-based scalable smart system for anomaly detection in pedestrian walkways. Computers & Electrical Engineering,2019, vol. 75,pp. 146–160. https://doi.org/10.1016/j.compeleceng.2019.02.017
Huang G.B., Zhu Q.Y., Siew C.K.Extreme learning machine: a new learning scheme of feedforward neural networks. Proc. of the 2004IEEE International Joint Conference on Neural Networks. V. 2,2004,pp. 985–990. https://doi.org/10.1109/ijcnn.2004.1380068
Fernández C., Salinas L., Torres C.E. A meta extreme learning machine method for forecasting financial time series. Applied Intelligence, 2019, vol. 49, no. 2, pp. 532–554. https://doi.org/10.1007/s10489-018-1282-3
Dehghani M., Montazeri Z., Malik O.P., Givi H., Guerrero J.M. Shell game optimization: A novel game-based algorithm. International Journal of Intelligent Engineering and Systems, 2020, vol. 13, no. 3, pp. 246–255. https://doi.org/10.22266/ijies2020.0630.23

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