Forecasting electrical power consumption using ARIMA method based on kWh of sold energy
(1) Gianika Roman Sosa Mail (Universitas Negeri Malang, Indonesia)
(2) Moh. Zainul Falah Mail (Universitas Negeri Malang, Indonesia)
(3) Dika Fikri L Mail (Universitas Negeri Malang, Indonesia)
(4) * Aji Prasetya Wibawa Mail (Universitas Negeri Malang, Indonesia)
(5) Anik Nur Handayani Mail (Universitas Negeri Malang, Indonesia)
(6) Jehad A. H. Hammad Mail (Al-Quds Open University, Palestinian Territory, Occupied)
*corresponding author

Abstract


Customer demand for electrical energy continues to increase, so electrical energy infrastructure must be developed to fulfill it. In order to generate and distribute electrical energy cost-effectively, it is crucial to estimate electrical energy consumption reasonably in advance. In addition, it is necessary to ensure that customer demands can be met and that there is no shortage of electricity supply. This study aims to determine the estimated long-term electricity use with a historical Energy Sold (T1) database in kW accumulated over several periods from 2008 to 2017. The ARIMA method with the Seasonal-ARIMA (SARIMA) pattern is used in forecasting analysis. The ARIMA method was chosen because it is considered appropriate for forecasting linear and univariate time-series data. The results of this study indicate that the MAPE (%) error rate is relatively low, with a result of 7,966, but the R-Square reaches a value of -0.024 due to the lack of observational data.

Keywords


Forecasting, ARIMA, Electrical energy consumption, MAPE

   

DOI

https://doi.org/10.31763/sitech.v2i1.637 		      

Article metrics

10.31763/sitech.v2i1.637 Abstract views : 1248 | PDF views : 311

   

Cite

How to cite item
   

Full Text

Download

References


K. M. Putra and S. Agustina, “Penentuan Kapasitas Motor Listrik sebagai Penggerak Generator pada Pembangkit Listrik Energi Listrik,” Sriwijaya University, 2018. Available: Google Scholar.
I. Al Rasyid, “Analisis Perilaku Konsumsi Energi Listrik Ditinjau Dalam Perspektif Ekonomi Islam (Studi Pada Masyarakat Kelurahan Tanjung Baru Kecamatan Kedamaian Kota Bandar Lampung),” UIN Raden Intan Lampung, 2020. Available: Google Scholar.
S. I. Kristianto and B. Mudakir, “Analisis Konsumsi Listrik Rumah Tangga di Kecamatan Tembalang,” Fakultas Ekonomika dan Bisnis, 2015. Available: Google Scholar.
E. H. S. Harahap, “Analisis Prakiraan Permintaan dan Penyediaan Energi Listrik Tahun 2019-2023 di Kabupaten Padang Lawas,” Unuversitas Islam Negeri Sultan Syarif Kasim Riau, 2019. Available: Google Scholar.
J. D. Jenkins, M. Luke, and S. Thernstrom, “Getting to Zero Carbon Emissions in the Electric Power Sector,” Joule, vol. 2, no. 12, pp. 2498–2510, Dec. 2018, doi: 10.1016/j.joule.2018.11.013.
J. Koskela, A. Rautiainen, and P. Järventausta, “Using electrical energy storage in residential buildings – Sizing of battery and photovoltaic panels based on electricity cost optimization,” Appl. Energy, vol. 239, pp. 1175–1189, Apr. 2019, doi: 10.1016/j.apenergy.2019.02.021.
R. R. Kumar and K. Alok, “Adoption of electric vehicle: A literature review and prospects for sustainability,” J. Clean. Prod., vol. 253, p. 119911, Apr. 2020, doi: 10.1016/j.jclepro.2019.119911.
V. Bianco, O. Manca, S. Nardini, and A. A. Minea, “Analysis and Forecasting of Nonresidential Electricity Consumption in Romania,” Appl. Energy, vol. 87, no. 11, pp. 3584–3590, Nov. 2010, doi: 10.1016/j.apenergy.2010.05.018.
I. A. Rahardjo, M. Subekti, P. Parjiman, and D. Rosyanti, “Analysis of electric load forecasting using combined method (Study case 2019 – 2029 in PT. PLN (Persero) UP3 Sukabumi),” {IOP} Conf. Ser. Mater. Sci. Eng., vol. 1098, no. 4, p. 42026, Mar. 2021, doi: 10.1088/1757-899x/1098/4/042026.
Y. Tanoto and M. Praptiningsih, “Factors Decomposition of Indonesia’s Household Electricity Consumption,” Eng. J., vol. 17, no. 2, pp. 19–28, Apr. 2013, doi: 10.4186/ej.2013.17.2.19.
M. Widiyanti, I. Sadalia, Z. Zunaidah, N. Irawati, and E. Hendrawaty, “Determining Firm’s Performance: Moderating Role of CSR in Renewable Energy Sector of Indonesia,” Polish J. Manag. Stud., vol. 19, no. 2, pp. 432–441, Jun. 2019, doi: 10.17512/pjms.2019.19.2.37.
R. Kurniawan and S. Managi, “Economic Growth and Sustainable Development in Indonesia: An Assessment,” Bull. Indones. Econ. Stud., vol. 54, no. 3, pp. 339–361, Sep. 2018, doi: 10.1080/00074918.2018.1450962.
H. do N. Camelo, P. S. Lucio, J. B. V. Leal Junior, P. C. M. de Carvalho, and D. von G. dos Santos, “Innovative hybrid models for forecasting time series applied in wind generation based on the combination of time series models with artificial neural networks,” Energy, vol. 151, pp. 347–357, May 2018, doi: 10.1016/j.energy.2018.03.077.
Y. Shao and Y.-S. Tsai, “Electricity Sales Forecasting Using Hybrid Autoregressive Integrated Moving Average and Soft Computing Approaches in the Absence of Explanatory Variables,” Energies, vol. 11, no. 7, p. 1848, Jul. 2018, doi: 10.3390/en11071848.
C. Wang, B.-B. Li, Q.-M. Liang, and J.-C. Wang, “Has China’s coal consumption already peaked? A demand-side analysis based on hybrid prediction models,” Energy, vol. 162, pp. 272–281, Nov. 2018, doi: 10.1016/j.energy.2018.08.031.
R. Adhikari and R. K. Agrawal, “An Introductory Study on Time Series Modeling and Forecasting,” Feb. 2013, doi: 10.48550/arXiv.1302.6613.
Y. Mao, A. Pranolo, A. P. Wibawa, A. B. Putra Utama, F. A. Dwiyanto, and S. Saifullah, “Selection of Precise Long Short Term Memory (LSTM) Hyperparameters based on Particle Swarm Optimization,” 2022 Int. Conf. Appl. Artif. Intell. Comput., pp. 1114–1121, May 2022, doi: 10.1109/ICAAIC53929.2022.9792708.
V. Bianco, O. Manca, and S. Nardini, “Electricity consumption forecasting in Italy using linear regression models,” Energy, vol. 34, no. 9, pp. 1413–1421, Sep. 2009, doi: 10.1016/j.energy.2009.06.034.
E. Erdogdu, “Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey,” Energy Policy, vol. 35, no. 2, pp. 1129–1146, Feb. 2007, doi: 10.1016/j.enpol.2006.02.013.
M. Tunç, Ü. Çamdali, and C. Parmaksizoğlu, “Comparison of Turkey’s electrical energy consumption and production with some European countries and optimization of future electrical power supply investments in Turkey,” Energy Policy, vol. 34, no. 1, pp. 50–59, Jan. 2006, doi: 10.1016/j.enpol.2004.04.027.
S. S. Pappas, L. Ekonomou, D. C. Karamousantas, G. E. Chatzarakis, S. K. Katsikas, and P. Liatsis, “Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models,” Energy, vol. 33, no. 9, pp. 1353–1360, Sep. 2008, doi: 10.1016/j.energy.2008.05.008.
V. S. Ediger and S. Akar, “ARIMA forecasting of primary energy demand by fuel in Turkey,” Energy Policy, vol. 35, no. 3, pp. 1701–1708, Mar. 2007, [Online]. Available: https://ideas.repec.org/a/eee/enepol/v35y2007i3p1701-1708.html.
F. Egelioglu, A. A. Mohamad, and H. Guven, “Economic variables and electricity consumption in Northern Cyprus,” Energy, vol. 26, no. 4, pp. 355–362, Apr. 2001, doi: 10.1016/S0360-5442(01)00008-1.
C. Zhang et al., “A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data,” Proc. AAAI Conf. Artif. Intell., vol. 33, pp. 1409–1416, Jul. 2019, doi: 10.1609/aaai.v33i01.33011409.
J. Fattah, L. Ezzine, Z. Aman, H. El Moussami, and A. Lachhab, “Forecasting of demand using ARIMA model,” Int. J. Eng. Bus. Manag., vol. 10, pp. 1–9, Jan. 2018, doi: 10.1177/1847979018808673.
A. L. Schaffer, T. A. Dobbins, and S.-A. Pearson, “Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions,” BMC Med. Res. Methodol., vol. 21, no. 1, p. 58, Dec. 2021, doi: 10.1186/s12874-021-01235-8.
N. Khan, A. Arshad, M. Azam, A. H. Al‐marshadi, and M. Aslam, “Modeling and forecasting the total number of cases and deaths due to pandemic,” J. Med. Virol., vol. 94, no. 4, pp. 1592–1605, Apr. 2022, doi: 10.1002/jmv.27506.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Gianika Roman Sosa, Moh. Zainul Falah, Dika Fikri L, Aji Prasetya Wibawa, Anik Nur Handayani, Jehad A. H. Hammad

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

___________________________________________________________
Science in Information Technology Letters
ISSN 2722-4139
Published by Association for Scientific Computing Electrical and Engineering (ASCEE)
W : http://pubs2.ascee.org/index.php/sitech
E : sitech@ascee.org, andri@ascee.org, andri.pranolo.id@ieee.org

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0

View My Stats