Analisis Prakiraan Beban Listrik Rumah Tangga dengan Menggunakan Metode Regresi
DOI:
https://doi.org/10.31963/intek.v6i2.1585Keywords:
load, residential, regression, linearAbstract
The purpose of this research is to analyse the number of consumers and electricity load of residential house in South Makassar region for 2019. To achieved this goal, a simple linear regression method for calculating the number of consumers and multiple linear regression methods to calculate the electrical load. To analyse this study, electricity data was taken from Electricity State Company (called PLN) since 2015 to 2016. Based on the results illustrated that the number of consumers and electricity load at the end of December 2019 were 752865 and 155187128 kWh, respectively.References
Imtikhan, N.,"Analisa Peramalan Kebutuhan Energi Listrik Pln Kota Surakarta Tahun 2011-2015 Untuk Konsumen Institution Jurusan Teknik Industri Fakultas Teknik Universitas Muhammadiyah Surakarta 2011
Gultom, T.T., "Pemenuhan Sumber Tenaga Listrik Di Indonesia ",Jurnal Ilmiah Research Sains Vol. 3, pp 130-138, 2017.
Hidayati, D.N.,"Perkiraan Kebutuhan Konsumsi Energi Listrik Di Kabupaten Pati Pada Tahun 2026 Dengan Menggunakan Metode Gabungan Institution Program Studi Teknik Elektro Fakultas Teknik Universitas Muhammadiyah Surakarta 2018
Insani, D.S., Badriana, and M. Daud, "Analisis Peramalan Kebutuhan Energi Listrik untuk Kabupaten Bireuen Menggunakan Perangkat Lunak LEAP",Jurnal Nasional Teknik Elektro, Vol. 8, pp 1-10, 2019.
Syafii and E. Noveri, "Studi Peramalan (Forecasting) Kurva Beban Harian Listrik Jangka Pendek Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA)",Jurnal Nasional Teknik Elektro, Vol. 2, pp 65-73, 2013.
Perdana, J.A., A. Soeprijanto, and R.S. Wibowo, "Peramalan Beban Listrik Jangka Pendek Menggunakan Optimally Pruned Extreme Learning Machine (OPELM) pada Sistem Kelistrikan Jawa Timur ",Jurnal Teknik ITS Vol. 1, pp 64-69, 2012.
Avci, E., W. Ketter, and E. Hecka, "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey",Energy Policy, Vol. 123, pp 390-403, 2018.
Lebotsa, M.E., et al., "Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem",Applied Energy, Vol. 222, pp 104-118, 2018.
D.H.Vu, K.M.Muttaqi, and A.P.Agalgaonkar, "A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables",Applied Energy, Vol. 140, pp 385-394, 2015.
Wibowo, H., Y. Mulyadi, and A.G. Abdullah, "Peramalan Beban Listrik Jangka Pendek Terklasifikasi Berbasis Metode Autoregressive Integrated Moving Average",ELECTRANS, Vol. 1, pp 2012.
Yuan, C., S. Liu, and Z. Fang, "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model",Energy, Vol. 100, pp 384-390, 2016.
Sulandari, W., et al., "Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks",Energy, Vol. pp 2019.
Kumar, U. and V.K.Jainb, "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India",Energy, Vol. 35, pp 1709-1716, 2010.
ErdemGünay, M., "Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey",Energy Policy, Vol. 90, pp 92-101, 2016.
Hassan, S., A. Khosravi, and J. Jaafar, "Examining performance of aggregation algorithms for neural network-based electricity demand forecasting",International Journal of Electrical Power & Energy Systems, Vol. 64, pp 1089-1105, 2015.
He, Y., et al., "Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network",Applied Energy, Vol. pp 565-575, 2019.
B.Yildiz, J.I.Bilbao, and A.B.Sproul, "A review and analysis of regression and machine learning models on commercial building electricity load forecasting",Renewable and Sustainable Energy Reviews, Vol. 73, pp 1104-1122, 2017.
Bianco, V., O. Manca, and SergioNardini, "Electricity consumption forecasting in Italy using linear regression models",Energy, Vol. 34, pp 1413-1421, 2009.
