Prediksi Konsumsi Listrik Jangka Menengah Menggunakan Algoritma Backpropagation
Studi Kasus di PT. PLN (Persero) Area Padang
Keywords:
Prediction, Consumption, Electricity, Population, PDRB, Backpropagation
Abstract
The availability of adequate and targeted electricity can accelerate the development of the region. Predicting the demand for electricity energy with a high degree of accuracy can help plan the production and distribution of electricity efficiently and efficiently. The study predicted the size of medium-term electricity consumption in the PLN (Persero) Area of Padang power system to help decision-making in generation and distribution planning. Backpropagation algorithms are an effective method that can be applied with a high degree of accuracy. Factors that can influence electricity consumption are used as input: economic growth, consumer growth, population and installed power. Meanwhile, electricity consumption rates are used as outputs. The data used are historical data of electricity consumption, customer growth, GDP over the period 2012-2017. Data 2012-2016 used as training data, data 2017 used as test data. Simulation and data processing using MATLAB software support. After processing and data analysis, the best architecture 4-2-1 with learning rate = 0.1, momentum = 0.9 yields the best prediction accuracy of 94.81 % and MAPE value = 5.1878. Proper prediction can help plan the supply of electricity needs. Backpropagation algorithms with correct parameters produce predictions with the best accuracy.
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References
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[3]. Feilat, Eyad & T. Al-Sha’abi, D & A. Momani, M. (2017). “Long-Term Load Forecasting Using Neural Network Approach for Jordan's Power System”. Engineering Press. 1. 43-50. DOI: 10.28964/EngPress-1-108.
[4]. Niswatul Arifah T, Murnomo Agus, Suryanto, Agus “Implementasi Neural Network pada Matlab untuk Prakiraan Konsumsi Beban Listrik Kabupaten Ponorogo Jawa Timur”, Jurnal Teknik Elektro Vol. 9 No. 1, 2017.
[5]. Rodygina S., Lyubchenko, V., Rodygin.A , "Efficiency of Using Artificial Neural Network for Short-Term Load Forecasting", Applied Mechanics and Materials, Vol.792, pp.312-316, 2015. DOI 10.4028/www.scientific.net/AMM.792.312
[6]. Srivastava, A. K., Pandey, A. S., & Singh, D, "Short-term load forecasting methods: A review". 2016 International Conference on Emerging Trends in Electrical Electronics & Sustainable Energy Systems (ICETEESES) 2016. doi:10.1109/iceteeses.2016.7581373
[7]. Nallathambi, S., Ramasamy, K “Prediction of electricity consumption based on DT and RF: An application on USA country power consumption”. 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE), 2017. doi:10.1109/iceice.2017.8191939
[8]. Rahman, A., Srikumar, V., & Smith, A. D., “Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks”. Applied Energy, 212, 372–385, 2018. doi:10.1016/j.apenergy.2017.12.051
[9]. P. Ozoh, S. Abd-Rahman and J. Labadin, "Predicting electricity consumption: A comparative analysis of the accuracy of various computational techniques," 2015 9th International Conference on IT in Asia (CITA), Kota Samarahan, 2015, pp. 1-7. doi : 10.1109/ CITA.2015.7349819
[10]. Kuster, C., Rezgui, Y., & Mourshed, M,” Electrical load forecasting models: A critical systematic review”. Sustainable Cities and Society, 35, 257–270, 2017. doi:10.1016/j.scs.2017.08.009
[11]. Khuntia, S. R., Rueda, J. L., & van der Meijden, M. A. M. M., “Forecasting the load of electrical power systems in mid- and long-term horizons: a review”, IET Generation, Transmission & Distribution, 10 (16) : 3971, 2016. DOI : 10.1049/iet-gtd.2016.0340
[12]. Negnevitsky , M., “Artificial Intelligence: A Guide to Intelligent Systems”, 2nd Edition, Addison-Wesley, 2004
[13]. Fausett, L. V. , “Fundamentals of Neural Networks: Architectures, Algorithms and Applications”, Prentice Hall, Englewood Cliffs, 1993
[14]. Handika Siregar, Y., “Prediksi Perilaku Pola Pengunjung Terhadap Transaksi Menggunakan Jaringan Syaraf Tiruan dengan Metode Backpropagation (Studi Kasus di Toko Sweet Amirah)”, Universitas Putra Indonesia “YPTK”, Padang 2015
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[16]. Herliyani Hasanah, Nurmalitasari, “Aplikasi Sistem Cerdas Untuk Prediksi Energi Listrik Pemakaian Sendiri Di PT. Indonesia Power Sub Unit PLTA Kabupaten Wonogiri“, Jurnal SIMETRIS, Vol 8 No 2 November 2017, DOI : 10.24176/simet.v8i2.1324
[17]. Hirsh, R. F., & Koomey, J. G. (). Electricity Consumption and Economic Growth: A New Relationship with Significant Consequences? The Electricity Journal, 28(9), 72–84. 2015. doi:10.1016/j.tej.2015.10.002
[18]. Kaytez, Fazil & Taplamacioglu, M & Çam, Ertuğrul & Hardalac, Firat., “Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines”, International Journal of Electrical Power & Energy Systems. 67, 2015. 10.1016/j.ijepes.2014.12.036.
[19]. Kim, Seong Pil, “MATLAB Deep Learning With Machine Learning, Neural Networks and Artificial Intelligence”, Apress, 2017. DOI : 10.1007/978-1-4842-2845-6
[20]. Kusumadewi, Sri; Hartati, Sri (), “Neuro-Fuzzy : Integrasi Sistem Fuzzy dan Jaringan Syaraf”, edisi 2, Graha Ilmu, 2010.
[21]. Luthuli, Q. W., & Folly, K. A. “Short term load forecasting using artificial intelligence”, 2016 IEEE PES PowerAfrica, 2016. doi:10.1109/powerafrica.2016.7556585
[22]. Made Nita D.S, I Wayan S, Ni Ketut Tari T., , “Peramalan Menggunakan Metode Backpropagation Neural Network”, E-Jurnal Matematika Vol. 7(3), Agustus 2018, pp. 264-270, 2018. DOI : 10.24843/MTK.2018.v07.i03.p213
[23]. Mahrufat D Olagoke, A A Ayeni and Moshood A Hambali. “Short Term Electric Load Forecasting using Neural Network and Genetic Algorithm”. International Journal of Applied Information Systems 10(4) : 22-28, January 2016. Doi : 10.5120/ijais2016451490
[24]. Muhammad Reza, - Suprayogi, “PREDIKSI JANGKA WAKTU PENGIRIMAN BARANG PADA PT. POS INDONESIA MENGGUNAKAN BACKPROPAGATION”, Cogito Smart Journal VOL. 3/NO. 1/JUNI 2017, DOI : 10.31154/cogito.v3i1.50.111-122
[25]. Mustika, Wida Prima, “PREDIKSI KONSUMSI ENERGI PADA BANGUNAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE BERBASIS ALGORITMA GENETIKA”, Jurnal Riset Sistem Informasi Dan Teknik Informatika (JURASIK) Volume (2) No. 1 Juli 2017, 2017 DOI : 10.30645/jurasik.v2i1.14
[26]. Neapolitan, Richard E., Jiang, Xia “Artificial Intelligence: With an Introduction to Machine Learning”, 2nd Edition, CRC Press, 2018.
[27]. Pouabe Eboule P.S., Hasan A.N., Twala B., “The Use of Multilayer Perceptron to Classify and Locate Power Transmission Line Faults”. In: Dash S., Naidu P., Bayindir R., Das S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 668. Springer, Singapore 2018. doi : 10.1007/978-981-10-7868-2_5
[28]. Prasetyo, Eko, “Data Mining – Konsep dan Aplikasi Menggunakan MATLAB”, Yogyakarta, Penerbit ANDI, 2012.
[29]. Riska Yanu Fa’rifah “BACKPROPAGATION NEURAL NETWORK UNTUK OPTIMASI AKURASI PADA PREDIKSI FINANCIAL DISTRESS PERUSAHAAN “, JURNAL INSTEK Vol 2, No 2, 2017. DOI : 10.24252/instek.v2i1.2609.g2461
[30]. Shen, M., Sun, H., & Lu, Y.. . “Household Electricity Consumption Prediction Under Multiple Behavioural Intervention Strategies Using Support Vector Regression”. Energy Procedia, 142, 2734–2739, 2017 . doi:10.1016/j.egypro.2017.12.218 \\
[31]. Siang, JJ, “Jaringan Syaraf Tiruan dan Pemrogramannya Menggunakan Matlab”, Andi, Yogyakarta, 2005
[32]. Singh, , Saurabh; Hussain, Shoeb; Bazaz, Mohammad Abid (2017), “Short Term Load Forecasting Using Artificial Neural Network”, IEEE 2017 Fourth International Conference on Image Information Processing (ICIIP) - Shimla, India, DOI : 10.1109/ICIIP.2017.8313703
[33]. Somantri, Oman & Supriyanto, Catur.. “Algoritme Genetika untuk Peningkatan Prediksi Kebutuhan Permintaan Energi Listrik”. Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI). 5. 108-114, 2016. DOI : 10.22146/jnteti.v5i2.233
[34]. Su, Fangchen; Xu, Yinliang; Tang, Xiaoying, “Short- and Mid-term Load Forecasting using Machine Learning Models”, 2017 China International Electrical and Energy Conference (CIEEC), 2017. DOI : 10.1109/CIEEC.2017.8388482
[35]. Vasudev Dehalwar ; Akhtar Kalam ; Mohan Lal Kolhe ; Aladin Zayegh,” Electricity load forecasting for Urban area using weather forecast information”, International Conference on Power and Renewable Energy, 2016. DOI 10.1109/ICPRE.2016.7871231
[36]. Wikarsa, M.T, “Studi Analisis Program Percepatan 10.000 MW Tahap I pada Operasi Sistem Tenaga Listrik Jawa Bali”, Universitas Indonesia, Jakarta, 2010
Published
2022-12-30
How to Cite
Yulef Dian. (2022). Prediksi Konsumsi Listrik Jangka Menengah Menggunakan Algoritma Backpropagation . TEMATIK, 9(2), 237-245. Retrieved from https://jurnal.plb.ac.id/index.php/tematik/article/view/2120
