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https://gestaoeproducao.com/article/doi/10.1590/1806-9649-2022v29e024
Gestão & Produção
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Forecasting electricity generation from renewable sources during a pandemic

Bianca Reichert; Adriano Mendonça Souza; Meiri Mezzomo

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Abstract

Abstract: Renewable sources are responsible for more than half of Brazilian electric generation, which basically correspond to hydropower, biomass and wind sources. This research aimed to verify if the Autoregressive Integrated Moving Average (ARIMA) models present good performance in predicting electricity generation from biomass, hydropower and wind power for the first months of COVID-19 pandemic in Brazil. The best forecasting models adjusted for biomass, hydropower and wind generation was the SARIMA, since this model was able to identify seasonal effects of climatic instability, such as periods of drought. Based on the seasonality of the largest generating sources, renewable generation needs to be offset by other sources, as non-renewable, and more efforts are needed to make Brazilian electric matrix more sustainable.

Keywords

ARIMA models, Renewable sources, Time series, COVID-19

Referências

Agência Nacional de Energia Elétrica – ANEEL. (2017). Programa de incentivo às fontes alternativas. Retrieved in 2021, July 7, from https://www.aneel.gov.br/proinfa

Agência Nacional de Energia Elétrica – ANEEL. (2020). Geração por fonte. Retrieved in 2021, July 1, from http://www.aneel.gov.br/dados/geracao

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. http://dx.doi.org/10.1109/TAC.1974.1100705.

Alsharif, M. H., Younes, M. K., & Kim, J. (2019). Time series ARIMA model for prediction of daily and monthly average global solar radiation: the case study of Seoul, South Korea. Symmetry, 11(2), 240. http://dx.doi.org/10.3390/sym11020240.

Aquila, G., Pamplona, E. O., Queiroz, A. R., Rotela, P., Jr., & Fonseca, M. N. (2017). An overview of incentive policies for the expansion of renewable energy generation in electricity power systems and the Brazilian experience. Renewable & Sustainable Energy Reviews, 70, 1090-1098. http://dx.doi.org/10.1016/j.rser.2016.12.013.

Bakhtiar, A., Aslani, A., & Hosseini, S. M. (2020). Challenges of diffusion and commercialization of bioenergy in developing countries. Renewable Energy, 145, 1780-1798. http://dx.doi.org/10.1016/j.renene.2019.06.126.

Baruque, B., Porras, S., Jove, E., & Calvo-Rolle, J. L. (2019). Geothermal heat exchanger energy prediction based on time series and monitoring sensors optimization. Energy, 171, 49-60. http://dx.doi.org/10.1016/j.energy.2018.12.207.

Bhutto, A. W., Bazmi, A. A., Khadija, Q., Harijan, K., Karim, S., & Ahmad, M. S. (2017). Forecasting the consumption of gasoline in transport sector in Pakistan based on ARIMA model. Environmental Progress & Sustainable Energy, 36(5), 1490-1497. http://dx.doi.org/10.1002/ep.12593.

Box, G. E. P., & Jenkins, G. M. (1970). Time series analysis, forecasting and control. San Francisco: Holden Day.

Box, G. E., Jenkins, G. M., & Reinsel, G. C. (1994). Time series analysis: forecasting and control (3rd ed.). New Jersey: Prentice Hall.

Carvalho, M., Delgado, D. B. M., Lima, K. M., Cencela, M. C., Siqueira, C. A., & Souza, D. L. B. (2021). Effects of the COVID-19 pandemic on the Brazilian electricity consumption patterns. International Journal of Energy Research, 45(2), 3358-3364. http://dx.doi.org/10.1002/er.5877.

Čepin, M. (2019). Evaluation of the power system reliability if a nuclear power plant is replaced with wind power plants. Reliability Engineering & System Safety, 185, 455-464. http://dx.doi.org/10.1016/j.ress.2019.01.010.

Cotia, B. P., Borges, C. L. T., & Diniz, A. L. (2019). Optimization of wind power generation to minimize operation costs in the daily scheduling of hydrothermal systems. International Journal of Electrical Power & Energy Systems, 113, 539-548. http://dx.doi.org/10.1016/j.ijepes.2019.05.071.

Croonenbroeck, C., & Stadtmann, G. (2019). Renewable generation forecast studies – Review and good practice guidance. Renewable & Sustainable Energy Reviews, 108, 312-322. http://dx.doi.org/10.1016/j.rser.2019.03.029.

Daioglou, V., Doelman, J. C., Wicke, B., Faaij, A., & van Vuuren, D. P. (2019). Integrated assessment of biomass supply and demand in climate change mitigation scenarios. Global Environmental Change, 54, 88-101. http://dx.doi.org/10.1016/j.gloenvcha.2018.11.012.

Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057-1072. http://dx.doi.org/10.2307/1912517.

Empresa de Pesquisa Energética – EPE. (2018). Brazilian energy balance 2018 year 2017. Rio de Janeiro: EPE.

Ferreira, J. H. I., Camacho, J. R., Malagoli, J. A., & Guimarães, S. C., Jr. (2016). Assessment of the potential of small hydropower development in Brazil. Renewable & Sustainable Energy Reviews, 56, 380-387. http://dx.doi.org/10.1016/j.rser.2015.11.035.

Ferreira, L. R. A., Otto, R. B., Silva, F. P., Souza, S. N. M., Souza, S. S., & Ando, O. H., Jr. (2018). Review of the energy potential of the residual biomass for the distributed generation in Brazil. Renewable & Sustainable Energy Reviews, 94, 440-455. http://dx.doi.org/10.1016/j.rser.2018.06.034.

Galvão, J., & Bermann, C. (2015). Crise hídrica e energia: conflitos no uso múltiplo das águas. Estudos Avançados, 29(84), 43-68. http://dx.doi.org/10.1590/S0103-40142015000200004.

González-Aparicio, I., & Zucker, A. (2015). Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain. Applied Energy, 159, 334-349. http://dx.doi.org/10.1016/j.apenergy.2015.08.104.

Haiges, R., Wang, Y. D., Ghoshray, A., & Roskilly, A. P. (2017). Forecasting electricity generation capacity in malaysia: an auto regressive integrated moving average approach. Energy Procedia, 105, 3471-3478. http://dx.doi.org/10.1016/j.egypro.2017.03.795.

Hosseini, S. M., Saifoddin, A., Shirmohammadi, R., & Aslani, A. (2019). Forecasting of CO2 emissions in Iran based on time series and regression analysis. Energy Reports, 5, 619-631. http://dx.doi.org/10.1016/j.egyr.2019.05.004.

International Energy Agency – IEA. (2017). Atlas of energy: share of renewables in total energy production (%). Retrieved in 2019, November 28, from http://energyatlas.iea.org/#!/tellmap/-1076250891/1

Jadhav, V., Reddy, B. V. C., & Gaddi, G. M. (2017). Application of ARIMA model for forecasting agricultural prices. Journal of Agricultural Science and Technology, 19(4), 981-992.

Jafarian-Namin, S., Goli, A., Qolipour, M., Mostafaeipour, A., & Golmohammadi, A. M. (2019). Forecasting the wind power generation using Box-Jenkins and hybrid artificial intelligence: a case study. International Journal of Energy Sector Management, 13(4), 1038-1062. http://dx.doi.org/10.1108/IJESM-06-2018-0002.

Khair, U., Fahmi, H., Al-Hakim, S., & Rahim, R. (2017). Forecasting error calculation with mean absolute deviation and mean absolute percentage error. Journal of Physics: Conference Series, 930, 012002. http://dx.doi.org/10.1088/1742-6596/930/1/012002.

Kim, H., Kim, S., Shin, H., & Heo, J. H. (2017). Appropriate model selection methods for nonstationary generalized extreme value models. Journal of Hydrology (Amsterdam), 547, 557-574. http://dx.doi.org/10.1016/j.jhydrol.2017.02.005.

Kuang, Y., Zhang, Y., Zhou, B., Li, C., Cao, Y., Li, L., & Zeng, L. (2016). A review of renewable energy utilization in islands. Renewable & Sustainable Energy Reviews, 59, 504-513. http://dx.doi.org/10.1016/j.rser.2016.01.014.

Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1-3), 159-178. http://dx.doi.org/10.1016/0304-4076(92)90104-Y.

Lucena, A. F. P., Hejazi, M., Vasquez-Arroyo, E., Turner, S., Köberle, A. C., Daenzer, K., Rochedo, P. R. R., Kober, T., Cai, Y., Beach, R. H., Gernaat, D., van Vuuren, D. P., & van der Zwaan, B. (2018). Interactions between climate change mitigation and adaptation: the case of hydropower in Brazil. Energy, 164, 1161-1177. http://dx.doi.org/10.1016/j.energy.2018.09.005.

Lucheroni, C., Boland, J., & Ragno, C. (2019). Scenario generation and probabilistic forecasting analysis of spatio-temporal wind speed series with multivariate autoregressive volatility models. Applied Energy, 239, 1226-1241. http://dx.doi.org/10.1016/j.apenergy.2019.02.015.

Maciel, P. N., Fo., Alcócer, J. C. A., Pinto, O. R. O., & Dolibaina, L. I. L. (2018). Sustainable energy public policies planning: encouraging the production and use of renewable energies. Revista Eletrônica em Gestão Educação e Tecnologia Ambiental, 22, 10. http://dx.doi.org/10.5902/2236117034211.

Medina, V. (2020). Incentivos fiscais na produção de energias renováveis. Moore.Retrieved in 2021, July 7, from https://www.moorebrasil.com.br/blog/incentivos-fiscais-na-producao-de-energias-renovaveis/

Mite-León, M., & Barzola-Monteses, J. (2018). Statistical model for the forecast of hydropower production in Ecuador. International Journal of Renewable Energy Research, 8(2), 1130-1137.

Morettin, P. A. (2016). Econometria financeira: um curso em séries temporais financeira (3ª ed.). São Paulo: Blucher.

Noronha, M. O., Zanini, R. R., & Souza, A. M. (2019). The impact of electric generation capacity by renewable and non-renewable energy in Brazilian economic growth. Environmental Science and Pollution Research International, 26(32), 33236-33259. http://dx.doi.org/10.1007/s11356-019-06241-4. PMid:31515770.

Olatunji, O., Akinlabi, S., Madushele, N., & Adedeji, P. A. (2019a). Estimation of Municipal Solid Waste (MSW) combustion enthalpy for energy recovery. EAI Endorsed Transactions on Energy Web, 6(23), 159119. http://dx.doi.org/10.4108/eai.11-6-2019.159119.

Olatunji, O., Akinlabi, S., Madushele, N., & Adedeji, P. A. (2019b). Estimation of the elemental composition of biomass using hybrid adaptive neuro-fuzzy inference system. BioEnergy Research, 12(3), 642-652. http://dx.doi.org/10.1007/s12155-019-10009-6.

Pes, M. P., Pereira, E. B., Marengo, J. A., Martins, F. R., Heinemann, D., & Schmidt, M. (2017). Climate trends on the extreme winds in Brazil. Renewable Energy, 109, 110-120. http://dx.doi.org/10.1016/j.renene.2016.12.101.

Qarnain, S. S., Sattanathan, M., Sankaranarayanan, B., & Ali, S. M. (2020). Analyzing energy consumption factors during coronavirus (COVID-19) pandemic outbreak: a case study of residential society. Energy Sources. Part A, Recovery, Utilization, and Environmental Effects, 1-20. http://dx.doi.org/10.1080/15567036.2020.1859651.

Ramser, C. A. S., Souza, A. M., Souza, F. M., Veiga, C. P., & Silva, W. V. (2019). The importance of principal components in studying mineral prices using vector autoregressive models: evidence from the Brazilian economy. Resources Policy, 62, 9-21. http://dx.doi.org/10.1016/j.resourpol.2019.03.001.

Razmjoo, A., Shirmohammadi, R., Davarpanah, A., Pourfayaz, F., & Aslani, A. (2019). Stand-alone hybrid energy systems for remote area power generation. Energy Reports, 5, 231-241. http://dx.doi.org/10.1016/j.egyr.2019.01.010.

Reichert, B., & Souza, A. M. (2020). Previsão e interação dos preços da celulose brasileira nos mercados interno e externo. Ciência Florestal, 30(2), 501-515. http://dx.doi.org/10.5902/1980509838223.

Reichert, B., & Souza, A. M. (2021). Interrelationship simulations among Brazilian electric matrix sources. Electric Power Systems Research, 193, 107019. http://dx.doi.org/10.1016/j.epsr.2020.107019.

Renn, O., & Marshall, J. P. (2016). Coal, nuclear and renewable energy policies in Germany: from the 1950s to the “Energiewende”. Energy Policy, 99, 224-232. http://dx.doi.org/10.1016/j.enpol.2016.05.004.

Saheli, M. A., Fazelpour, F., Soltani, N., & Rosen, M. A. (2019). Performance analysis of a photovoltaic/wind/diesel hybrid power generation system for domestic utilization in winnipeg, manitoba, Canada. Environmental Progress & Sustainable Energy, 38(2), 548-562. http://dx.doi.org/10.1002/ep.12939.

Salles, A. A., & Campanati, A. B. M. (2019). The relevance of crude oil prices on natural gas pricing expectations: a dynamic model based empirical study. International Journal of Energy Economics and Policy, 9(5), 322-330. http://dx.doi.org/10.32479/ijeep.7755.

Senna, V., & Souza, A. M. (2016). Assessment of the relationship of government spending on social assistance programs with Brazilian macroeconomic variables. Physica A, 462, 21-30. http://dx.doi.org/10.1016/j.physa.2016.05.022.

Shen, Z., & Ritter, M. (2016). Forecasting volatility of wind power production. Applied Energy, 176, 295-308. http://dx.doi.org/10.1016/j.apenergy.2016.05.071.

Silva, A. R., Pimenta, F. M., Assireu, A. T., & Spyrides, M. H. C. (2016). Complementarity of Brazil׳s hydro and offshore wind power. Renewable & Sustainable Energy Reviews, 56, 413-427. http://dx.doi.org/10.1016/j.rser.2015.11.045.

Souza, F. M. (2016). Modelos de previsão: aplicações à energia elétrica ARIMA-ARCH-AI e ACP (1ª ed.). Curitiba: Appris.

Uddin, M. N., Taweekun, J., Techato, K., Rahman, M. A., Mofijur, M., & Rasul, M. G. (2019). Sustainable biomass as an alternative energy source: bangladesh perspective. Energy Procedia, 160, 648-654. http://dx.doi.org/10.1016/j.egypro.2019.02.217.
 

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