Implementation of Artificial Neural Network Method for Estimating Connected Power and Electric Energy Consumption

Roub Nizaar, Anik Nur Handayani

Abstract


Abstract—Electricity is vital for modern society’s welfare. Daily electricity usage depended on the customers’ type. Hence, there was a difference between the connected power with consumption. Therefore, there needed an estimation method for long-term connected power and energy consumption to improve the safety of energy management and operation plan for the generator. This research used the Artificial Neural Network method with a backpropagation algorithm model to estimate the connected power and electricity consumption. This method has the advantage of following past patterns after the training process. This research used data such as total population, Gross Regional Domestic Product, total customers, produced energy, remaining energy, distribution loss, total transformer, peak load, and load factor as the independent data. The energy consumption and connected power served as the dependent data. The data was taken from Srengat Network Service Unit, East Java, for ten years, which started in 2008. This research used literature study, information and data collection, information and data process, data estimation and analysis, and conclusion as the procedures. Based on the results, the best network structure was 9-9-2 with the 10-6 goal, 0.9 momentum value, and 0.15 learning rate to produce the smallest Mean Squared Error of 0.00442 in 2015, Mean Absolute Percentage Error of 7.88% for the connected power, and 11.27% on electricity consumption target.

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References


F. Corcelli et al., ―Sustainable urban electricity supply chain – Indicators of material recovery and energy savings from crystalline silicon photovoltaic panels end-of-life,‖ Ecol. Indic., vol. 94, pp. 37–51, Nov. 2018, doi: 10.1016/j.ecolind.2016.03.028.

M. C. Carnero and A. Gómez, ―Maintenance strategy selection in electric power distribution systems,‖ Energy, vol. 129, pp. 255–272, Jun. 2017, doi: 10.1016/j.energy.2017.04.100.

S.-Y. Huh, M. Jo, J. Shin, and S.-H. Yoo, ―Impact of rebate program for energy-efficient household appliances on consumer purchasing decisions: The case of electric rice cookers in South Korea,‖ Energy Policy, vol. 129, pp. 1394–1403, Jun. 2019, doi: 10.1016/j.enpol.2019.03.049.

Y. Sun, L. Zhu, Z. Xu, L. Xiao, J. Zhang, and J. Zhang, ―Characteristic analysis and forecast of electricity supply and demand in APEC,‖ Glob. Energy Interconnect., vol. 2, no. 5, pp. 413–422, Oct. 2019, doi: 10.1016/j.gloei.2019.11.016.

S. Hr. A. Kaboli, J. Selvaraj, and N. A. Rahim, ―Long-term electric energy consumption forecasting via artificial cooperative search algorithm,‖ Energy, vol. 115, pp. 857–871, Nov. 2016, doi: 10.1016/j.energy.2016.09.015.

L. M. Korunović, A. S. Jović, and S. Z. Djokic, ―Measurement-based evaluation of static load characteristics of demands in administrative buildings,‖ Int. J. Electr. Power Energy Syst., vol. 118, p. 105782, Jun. 2020, doi: 10.1016/j.ijepes.2019.105782.

L. T. Al-Bahrani, B. Horan, M. Seyedmahmoudian, and A. Stojcevski, ―Dynamic economic emission dispatch with load demand management for the load demand of electric vehicles during crest shaving and valley filling in smart cities environment,‖ Energy, p. 116946, Jan. 2020, doi: 10.1016/j.energy.2020.116946.

V. M. Fthenakis, ―End-of-life management and recycling of PV modules,‖ Energy Policy, vol. 28, no. 14, pp. 1051–1058, Nov. 2000, doi: 10.1016/S0301-4215(00)00091-4.

L. Geng, Z. Lu, L. He, J. Zhang, X. Li, and X. Guo, ―Smart charging management system for electric vehicles in coupled transportation and power distribution systems,‖ Energy, vol. 189, p. 116275, Dec. 2019, doi: 10.1016/j.energy.2019.116275.

A. V. H. Sola and C. M. M. Mota, ―Influencing factors on energy management in industries,‖ J. Clean. Prod., vol. 248, p. 119263, Mar. 2020, doi: 10.1016/j.jclepro.2019.119263.

S. Aznavi, P. Fajri, R. Sabzehgarm, and A. Asrari, ―Optimal management of residential energy storage systems in presence of intermittencies,‖ J. Build. Eng., p. 101149, Dec. 2019, doi: 10.1016/j.jobe.2019.101149.

S. Mousavian, J. Valenzuela, and J. Wang, ―Real-time data reassurance in electrical power systems based on artificial neural networks,‖ Electr. Power Syst. Res., vol. 96, pp. 285–295, Mar. 2013, doi: 10.1016/j.epsr.2012.11.015.

R. Hooshmand and M. Moazzami, ―Optimal design of adaptive under frequency load shedding using artificial neural networks in isolated power system,‖ Int. J. Electr. Power Energy Syst., vol. 42, no. 1, pp. 220–228, Nov. 2012, doi: 10.1016/j.ijepes.2012.04.021.

S. M. Ashraf, A. Gupta, D. K. Choudhary, and S. Chakrabarti, ―Voltage stability monitoring of power systems using reduced network and artificial neural network,‖ Int. J. Electr. Power Energy Syst., vol. 87, pp. 43–51, May 2017, doi: 10.1016/j.ijepes.2016.11.008.

M. Talaat, M. H. Gobran, and M. Wasfi, ―A hybrid model of an artificial neural network with thermodynamic model for system diagnosis of electrical power plant gas turbine,‖ Eng. Appl. Artif. Intell., vol. 68, pp. 222–235, Feb. 2018, doi: 10.1016/j.engappai.2017.10.014.

J.-H. Menke, N. Bornhorst, and M. Braun, ―Distribution system monitoring for smart power grids with distributed generation using artificial neural networks,‖ Int. J. Electr. Power Energy Syst., vol. 113, pp. 472–480, Dec. 2019, doi: 10.1016/j.ijepes.2019.05.057.

A. de Myttenaere, B. Golden, B. Le Grand, and F. Rossi, ―Mean Absolute Percentage Error for regression models,‖ Neurocomputing, vol. 192, pp. 38–48, Jun. 2016, doi: 10.1016/j.neucom.2015.12.114.

S. Kim and H. Kim, ―A new metric of absolute percentage error for intermittent demand forecasts,‖ Int. J. Forecast., vol. 32, no. 3, pp. 669–679, Jul. 2016, doi: 10.1016/j.ijforecast.2015.12.003.

L. Frías-Paredes, F. Mallor, M. Gastón-Romeo, and T. León, ―Dynamic mean absolute error as new measure for assessing forecasting errors,‖ Energy Convers. Manag., vol. 162, pp. 176–188, Apr. 2018, doi: 10.1016/j.enconman.2018.02.030.




DOI: http://dx.doi.org/10.17977/um049v1i2p13-18

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