Energy Communities from Municipality Level to Province Level: A Comparison Using Autoregressive Integrated Moving Average Model
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Energy Communities from Municipality Level to Province Level: A Comparison Using Autoregressive Integrated Moving Average Model

Authors: Amro Issam Hamed Attia Ramadan, Marco Zappatore, Pasquale Balena, Antonella Longo

Abstract:

Considering the energy crisis that is hitting Europe, it becomes increasingly necessary to change energy policies to depend less on fossil fuels and replace them with energy from renewable sources. This has triggered the urge to use clean energy, not only to satisfy energy needs and fulfill the required consumption, but also to decrease the danger of climatic changes due to harmful emissions. Many countries have already started creating energy communities based on renewable energy sources. The first step to understanding energy needs in any place is to perfectly know the consumption. In this work, we aim to estimate electricity consumption for a municipality that makes up part of a rural area located in southern Italy using forecast models that allow for the estimation of electricity consumption for the next 10 years, and we then apply the same model to the province where the municipality is located and estimate the future consumption for the same period to examine whether it is possible to start from the municipality level to reach the province level when creating energy communities.

Keywords: ARIMA, electricity consumption, forecasting models, time series.

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References:


[1] A. A. E1-Keib, X. Ma, and H. Ma, “Advancement of statistical based modeling techniques for short-term load forecasting,” 1995.
[2] A. Azadeh, S. F. Ghaderi, and S. Sohrabkhani, “Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors,” Energy Convers Manag, vol. 49, no. 8, pp. 2272–2278, Aug. 2008, doi: 10.1016/j.enconman.2008.01.035.
[3] G. Xu and W. Wang, “Forecasting China’s natural gas consumption based on a combination model,” Journal of Natural Gas Chemistry, vol. 19, no. 5, pp. 493–496, Sep. 2010, doi: 10.1016/S1003-9953(09)60100-6.
[4] M. Kankal, A. Akpinar, M. I. Kömürcü, and T. Ş. Özşahin, “Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables,” Appl Energy, vol. 88, no. 5, pp. 1927–1939, 2011, doi: 10.1016/j.apenergy.2010.12.005.
[5] C. S. Lin, F. M. Liou, and C. P. Huang, “Grey forecasting model for CO2 emissions: A Taiwan study,” Appl Energy, vol. 88, no. 11, pp. 3816–3820, 2011, doi: 10.1016/j.apenergy.2011.05.013.
[6] F. Shaikh and Q. Ji, “Forecasting natural gas demand in China: Logistic modelling analysis,” International Journal of Electrical Power and Energy Systems, vol. 77, pp. 25–32, May 2016, doi: 10.1016/j.ijepes.2015.11.013.
[7] Y. R. Zeng, Y. Zeng, B. Choi, and L. Wang, “Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network,” Energy, vol. 127, pp. 381–396, 2017, doi: 10.1016/j.energy.2017.03.094.
[8] T. Ahmad, H. Zhang, and B. Yan, “A review on renewable energy and electricity requirement forecasting models for smart grid and buildings,” Sustainable Cities and Society, vol. 55. Elsevier Ltd, Apr. 01, 2020. doi: 10.1016/j.scs.2020.102052.
[9] Choden and S. Unhapipat, “ARIMA model to forecast international tourist visit in Bumthang, Bhutan,” in Journal of Physics: Conference Series, Jun. 2018, vol. 1039, no. 1. doi: 10.1088/1742-6596/1039/1/012023.
[10] H. Wang, J. Huang, H. Zhou, L. Zhao, and Y. Yuan, “An integrated variational mode decomposition and ARIMA model to forecast air temperature,” Sustainability (Switzerland), vol. 11, no. 15, Aug. 2019, doi: 10.3390/su11154018.
[11] M. R. Abonazel and A. I. Abd-Elftah, “Forecasting Egyptian GDP using ARIMA models,” Reports on Economics and Finance, vol. 5, no. 1, pp. 35–47, 2019, doi: 10.12988/ref.2019.81023.
[12] G. Perone, “An ARIMA Model to Forecast the Spread and the Final Size of COVID-2019 Epidemic in Italy 1”, doi: 10.1101/2020.04.27.20081539.
[13] S. I. Alzahrani, I. A. Aljamaan, and E. A. Al-Fakih, “Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions,” J Infect Public Health, vol. 13, no. 7, pp. 914–919, Jul. 2020, doi: 10.1016/j.jiph.2020.06.001.
[14] A. F. Lukman, R. I. Rauf, O. Abiodun, O. Oludoun, K. Ayinde, and R. O. Ogundokun, “COVID-19 prevalence estimation: Four most affected African countries,” Infect Dis Model, vol. 5, pp. 827–838, Jan. 2020, doi: 10.1016/j.idm.2020.10.002.
[15] S. Noureen, S. Atique, V. Roy, and S. Bayne, “Analysis and application of seasonal ARIMA model in Energy Demand Forecasting: A case study of small scale agricultural load,” in Midwest Symposium on Circuits and Systems, Aug. 2019, vol. 2019-August, pp. 521–524. doi: 10.1109/MWSCAS.2019.8885349.
[16] S. Ozturk and F. Ozturk, “Forecasting Energy Consumption of Turkey by Arima Model,” Journal of Asian Scientific Research, vol. 8, no. 2, pp. 52–60, 2018, doi: 10.18488/journal.2.2018.82.52.60.
[17] Y. Wang, J. Wang, G. Zhao, and Y. Dong, “Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China,” Energy Policy, vol. 48, pp. 284–294, Sep. 2012, doi: 10.1016/j.enpol.2012.05.026.
[18] I. Mado, A. Soeprijanto, and S. Suhartono, “Applying of Double Seasonal ARIMA Model for Electrical Power Demand Forecasting at PT. PLN Gresik Indonesia,” International Journal of Electrical and Computer Engineering (IJECE), vol. 8, no. 6, p. 4892, Dec. 2018, doi: 10.11591/ijece.v8i6.pp4892-4901.
[19] F. Jiang, X. Yang, and S. Li, “Comparison of forecasting India’s energy demand using an MGM, ARIMA model, MGM-ARIMA model, and BP neural network model,” Sustainability (Switzerland), vol. 10, no. 7, Jun. 2018, doi: 10.3390/su10072225.
[20] J. Miao, “Based on ARIMA model for the energy consumption forecasting in China,” 2015.
[21] M. Găiceanu, C. S. Universitatea Dunărea de Jos Galați. Faculty of Automation, IEEE Romania Section. CAS/CS Joint Chapter, IEEE Romania Section. Power Electronics Chapter, and Institute of Electrical and Electronics Engineers, ISEEE-2017: 2017 5th International Symposium on Electrical and Electronics Engineering (ISEEE): proceedings (Xplore compliant): October 20-22, 2017, Galați, Romania.
[22] S. Li and R. Li, “Comparison of forecasting energy consumption in Shandong, China Using the ARIMA model, GM model, and ARIMA-GM model,” Sustainability (Switzerland), vol. 9, no. 7, Jul. 2017, doi: 10.3390/su9071181.
[23] B. Nepal, M. Yamaha, A. Yokoe, and T. Yamaji, “Electricity load forecasting using clustering and ARIMA model for energy management in buildings,” Japan Architectural Review, vol. 3, no. 1, pp. 62–76, Jan. 2020, doi: 10.1002/2475-8876.12135.
[24] E. E. and T. Union of Electronics, Institute of Electrical and Electronics Engineers. Bulgaria Section, and Institute of Electrical and Electronics Engineers, 2019 16th Conference on Electrical Machines, Drives and Power Systems (ELMA): ELMA 2019: proceedings: 6-8 June 2019, Varna, Bulgaria.
[25] M. Elsaraiti, G. Ali, H. Musbah, A. Merabet, and T. Little, “Time series analysis of electricity consumption forecasting using ARIMA model,” in IEEE Green Technologies Conference, Apr. 2021, vol. 2021-April, pp. 259–262. doi: 10.1109/GreenTech48523.2021.00049.
[26] IEEE Computer Society and Institute of Electrical and Electronics Engineers, 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI): 24-25 December, Dhaka.
[27] V. Ş. 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, doi: 10.1016/j.enpol.2006.05.009.
[28] M. Akpinar and N. Yumusak, “Forecasting household natural gas consumption with ARIMA model: A case study of removing cycle,” in AICT 2013 - 7th International Conference on Application of Information and Communication Technologies, Conference Proceedings, 2013. doi: 10.1109/ICAICT.2013.6722753.
[29] “RapidMiner | Amplify the Impact of Your People, Expertise & Data.” https://rapidminer.com/ (accessed Nov. 13, 2022).
[30] “Statistiche - Terna spa.” https://www.terna.it/it/sistema-elettrico/statistiche/pubblicazioni-statstiche (accessed Nov. 13, 2022).
[31] “Istat.it.” https://www.istat.it/ (accessed Nov. 13, 2022).
[32] “Autorità per l’energia elettrica e il gas.” (Online). Available: www.autorita.energia.it.