Araştırma Makalesi
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Yıl 2021, Cilt: 11 Sayı: 2, 126 - 135, 30.12.2021
https://doi.org/10.36222/ejt.894463

Öz

Kaynakça

  • [1] Mofijur, M., Fattah, I. R., Alam, M. A., Islam, A. S., Ong, H. C., Rahman, S. A., Mahlia, T. M. I. (2020). Impact of COVID-19 on the social, economic, environmental and energy domains: Lessons learnt from a global pandemic. Sustainable production and consumption.
  • [2] Steffen, B., Egli, F., Pahle, M., Schmidt, T. S. (2020). Navigating the clean energy transition in the COVID-19 crisis. Joule, 4(6), 1137-1141.
  • [3] Rosenbloom, D., Markard, J. (2020). A COVID-19 recovery for climate.
  • [4] Hosseini, S. E. (2020). An outlook on the global development of renewable and sustainable energy at the time of Covid-19. Energy Research Social Science, 68, 101633.
  • [5] Rapaccini, M., Saccani, N., Kowalkowski, C., Paiola, M., Adrodegari, F. (2020). Navigating disruptive crises through service-led growth: The impact of COVID-19 on Italian manufacturing firms. Industrial Marketing Management, 88, 225-237.
  • [6] Donthu, N. Gustafsson, A. (2020). Effects of COVID-19 on business and research. Journal of business research, 117, 284.
  • [7] Elavarasan, R. M., Shafiullah, G. M., Raju, K., Mudgal, V., Arif, M. T., Jamal, T., ... Subramaniam, U. (2020). COVID-19: Impact analysis and recommendations for power sector operation. Applied energy, 279, 115739.
  • [8] Sovacool, B. K., Del Rio, D. F., Griffiths, S. (2020). Contextualizing the Covid-19 pandemic for a carbon-constrained world: Insights for sustainability transitions, energy justice, and research methodology. Energy Research Social Science, 68, 101701.
  • [9] Polemis, M., Soursou, S. (2020). Assessing the impact of the COVID-19 pandemic on the Greek energy firms: An event study analysis. Energy Research Letters, 1(3), 17238.
  • [10] Akrofi, M. M., Antwi, S. H. (2020). COVID-19 energy sector responses in Africa: a review of preliminary government interventions. Energy Research Social Science, 68, 101681.
  • [11] Wolff, S., Ladi, S. (2020). European Union Responses to the Covid-19 Pandemic: adaptability in times of Permanent Emergency. Journal of European Integration, 42(8), 1025-1040.
  • [12] Demirbilek, Y., Pehlivantürk, G., Özgüler, Z. Ö., Mese, E. A. (2020). COVID-19 outbreak control, example of ministry of health of Turkey. Turkish journal of medical sciences, 50(SI-1), 489-494.
  • [13] Abu-Rayash, A., Dincer, I. (2020). Analysis of the electricity demand trends amidst the COVID-19 coronavirus pandemic. Energy Research & Social Science, 68, 101682.
  • [14] Werth, A., Gravino, P., Prevedello, G. (2021). Impact analysis of COVID-19 responses on energy grid dynamics in Europe. Applied energy, 281, 116045.
  • [15] Irena April 2020, Global Renewables Outlook: Energy transformation 2050, Page:15, 34.
  • [16] Analytica, O. (2021). Climate change demands drastic action internationally. Expert Briefings.
  • [17] Selmi, R., Bouoiyour, J., Hammoudeh, S., Errami, Y., Wohar, M. E. (2021). The energy transition, Trump energy agenda and COVID-19. International Economics, 165, 140-153.
  • [18] Wärtsilä Corporation, news & press releases, 17 April 2020.
  • [19] Mike Fulwood, March 2020, $2 gas in Europe is here: who will blink first?, Oxfordenergy.
  • [20] NAB Group Economics, NAB MINERALS AND ENERGY OUTLOOK, JUNE 2020.
  • [21] Ember, Daily EU ETS carbon market price (Euros) , https://ember-climate.org/data/carbon-price-viewer/
  • [22] Erdin, C., Ozkaya, G. (2019). Turkey’s 2023 energy strategies and investment opportunities for renewable energy sources: site selection based on electre. Sustainability, 11(7), 2136.
  • [23] https://www.imf.org/en/Publications/WEO/Issues/2021/01/26/2021-world-economic-outlook-update
  • [24] Yilmaz, Musa, Kilic, Heybet, “Smart grid road map and challenges for Turkey” IET Digital Library, Microgrids for Rural Areas, page:389-420, (2020), ISBN:9781785619991
  • [25] https://www.intelligentliving.co/turkey-new-record-renewable-energy/
  • [26] Yılmaz, Musa, "Real Measure of a Transmission Line Data with Load Fore-cast Model for The Future". Balkan Journal of Electrical and Computer Engineering 6 / 2 (April 2018): 141-145.
  • [27] Deb, C., Zhang, F., Yang, J., Lee, S. E., Shah, K. W. (2017). A review on time series forecasting techniques for building energy consumption. Renewable and Sustainable Energy Reviews, 74, 902-924.
  • [28] Ghofrani, M., Suherli, A. (2017). Time series and renewable energy forecasting. Time Ser. Anal. Appl, 2017, 77-92.
  • [29] Berthou, T., Stabat, P., Salvazet, R., Marchio, D. (2014). Development and validation of a gray box model to predict thermal behavior of occupied office buildings. Energy and Buildings, 74, 91-100.
  • [30] Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A., & Hyndman, R. J. (2016). Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond.
  • [31] Rahmani, R., Yusof, R., Seyedmahmoudian, M., Mekhilef, S. (2013). Hybrid technique of ant colony and particle swarm optimization for short term wind energy forecasting. Journal of Wind Engineering and Industrial Aerodynamics, 123, 163-170.
  • [32] Kıran, M. S., Ozceylan, E., Gunduz, M., Paksoy, T. (2012). A novel hybrid approach based on particle swarm optimization and ant colony algorithm to forecast energy demand of Turkey. Energy conversion and management, 53(1), 75-83.
  • [33] AlRashidi, M. R., El-Naggar, K. M. (2010). Long term electric load forecasting based on particle swarm optimization. Applied Energy, 87(1), 320-326.
  • [34] Azadeh, A., Tarverdian, S. (2007). Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption. Energy policy, 35(10), 5229-5241.
  • [35] Viswavandya, M., Mohanty, A. (2018). Fuzzy Logic and ANFIS based Short Term Solar Energy Forecasting. Int. J. Futur. Revolut. Comput. Sci. Commun. Eng., 4, 631-636.
  • [36] Runge, J., Zmeureanu, R. (2019). Forecasting energy use in buildings using artificial neural networks: A review. Energies, 12(17), 3254.
  • [37] Suganthi, L., Samuel, A. A. (2012). Energy models for demand forecasting—A review. Renewable and sustainable energy reviews, 16(2), 1223-1240.
  • [38] Dumitru, C. D., Gligor, A. (2017). Daily average wind energy forecasting using artificial neural networks. Procedia Engineering, 181, 829-836.
  • [39] Galván, I. M., Valls, J. M., Cervantes, A., Aler, R. (2017). Multi-objective evolutionary optimization of prediction intervals for solar energy forecasting with neural networks. Information Sciences, 418, 363-382.
  • [40] Jasiński, T. (2019). Modeling electricity consumption using nighttime light images and artificial neural networks. Energy, 179, 831-842.
  • [41] Alanis, A. Y. (2018). Electricity prices forecasting using artificial neural networks. IEEE Latin America Transactions, 16(1), 105-111.
  • [42] Akarslan, E., Hocaoglu, F. O. (2018, March). Electricity demand forecasting of a micro grid using ANN. In 2018 9th International Renewable Energy Congress (IREC) (pp. 1-5). IEEE.
  • [43] Gajowniczek, K., Nafkha, R., Ząbkowski, T. (2017, September). Electricity peak demand classification with artificial neural networks. In 2017 Federated Conference on Computer Science and Information Systems (FedCSIS) (pp. 307-315). IEEE.
  • [44] Yasin Çodur, M., Ünal, A. (2019). An estimation of transport energy demand in Turkey via artificial neural networks. Promet-Traffic&Transportation, 31(2), 151-161.
  • [45] Martellotta, F., Ayr, U., Stefanizzi, P., Sacchetti, A., Riganti, G. (2017). On the use of artificial neural networks to model household energy consumptions. Energy Procedia, 126, 250-257.
  • [46] Han, T., Jiang, D., Zhao, Q., Wang, L., Yin, K. (2018). Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery. Transactions of the Institute of Measurement and Control, 40(8), 2681-2693.
  • [47] Metaxiotis, K., Kagiannas, A., Askounis, D., Psarras, J. (2003). Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher. Energy conversion and Management, 44(9), 1525-1534.
  • [48] Hippert, H. S., Pedreira, C. E., Souza, R. C. (2001). Neural networks for short-term load forecasting: A review and evaluation. IEEE Transactions on power systems, 16(1), 44-55.
  • [49] Jamei, M., Nisnevich, A., Wetchler, E., Sudat, S., Liu, E. (2017). Predicting all-cause risk of 30-day hospital readmission using artificial neural networks. PloS one, 12(7), e0181173.
  • [50] Saba, A. I., Elsheikh, A. H. (2020). Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks. Process safety and environmental protection, 141, 1-8.
  • [51] Hamadneh, N. N., Khan, W. A., Ashraf, W., Atawneh, S. H., Khan, I., Hamadneh, B. N. (2021). Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia. Comput. Mater. Contin, 66, 2787-2796.
  • [52] Sahoo, A., Singh, U. K., Kumar, M. H., Samantaray, S. (2021). Estimation of Flood in a River Basin Through Neural Networks: A Case Study. In Communication Software and Networks (pp. 755-763). Springer, Singapore.
  • [53] Pillai, S. R., Yadav, A., Vashishtha, V. K. (2021). Prediction of Wind Power Curve Based on Wind Speed and Direction Utilizing Artificial Neural Network. In Recent Advances in Mechanical Engineering (pp. 515-522). Springer, Singapore.
  • [54] Jena, P. R., Majhi, R., Kalli, R., Managi, S., Majhi, B. (2021). Impact of COVID-19 on GDP of major economies: Application of the artificial neural network forecaster. Economic Analysis and Policy, 69, 324-339.
  • [55] Ataei, A., Feghhi, T., Leventouri, T., Muhammad, D. W. (2021). Liver Cancer Risk Quantification through Artificial Neural Network. Bulletin of the American Physical Society.
  • [56] Noori, N., Kalin, L., Isik, S. (2020). Water quality prediction using SWAT-ANN coupled approach. Journal of Hydrology, 590, 125220.
  • [57] Li, B., Lee, Y., Yao, W., Lu, Y., Fan, X. (2020). Development and application of ANN model for property prediction of supercritical kerosene. Computers Fluids, 209, 104665.
  • [58] Verma, A., Prakash, S., Kumar, A. (2020). ANN‐based energy consumption prediction model up to 2050 for a residential building: Towards sustainable decision making. Environmental Progress Sustainable Energy, e13544.
  • [59] Fan, C., Ding, Y. (2019). Cooling load prediction and optimal operation of HVAC systems using a multiple nonlinear regression model. Energy and Buildings, 197, 7-17.
  • [60] Goldthorpe, J. H., Goldthorpe, O. F. J. H. (2000). On sociology: Numbers, narratives, and the integration of research and theory. Oxford University Press on Demand.
  • [61] Moosa, I. A. (2017). Econometrics as a con art: exposing the limitations and abuses of econometrics. Edward Elgar Publishing.
  • [62] https://tradingeconomics.com/turkey/gdp-growth-annual
  • [63] https://www.epdk.gov.tr/Detay/Icerik/3-0-104/aylik-sektor-raporu
  • [64] https://ytbsbilgi.teias.gov.tr/ytbsbilgi/frm_istatistikler.jsf
  • [65] https://covid19.who.int/region/euro/country/tr
  • [66] https://ourworldindata.org/coronavirus/country/turkey?country=~TUR
  • [67] https://tradingeconomics.com/turkey/manufacturing-pmi
  • [68] https://tradingeconomics.com/turkey/exports
  • [69] https://tradingeconomics.com/turkey/foreign-direct-investment
  • [70] https://tradingeconomics.com/turkey/industrial-production
  • [71] https://tr.investing.com/indices/ise-100-historical-data
  • [72] Satrovic, E. (2017). Financial development and human capital in Turkey: ARDL approach. Kapadokya Akademik Bakıs, 1(2), 1-15.
  • [73] Çetinkaya, Z., Erdal, E. (2019, September). Daily Food Demand Forecast with Artificial Neural Networks: Kırıkkale University Case. In 2019 4th International Conference on Computer Science and Engineering (UBMK) (pp. 1-6). IEEE.
  • [74] Norouzi, N., de Rubens, G. Z., Choubanpishehzafar, S., Enevoldsen, P. (2020). When pandemics impact economies and climate change: exploring the impacts of COVID-19 on oil and electricity demand in China. Energy Research & Social Science, 68, 101654.
  • [75] Bartik, A. W., Cullen, Z. B., Glaeser, E. L., Luca, M., Stanton, C. T. (2020). What jobs are being done at home during the COVID-19 crisis? Evidence from firm-level surveys (No. w27422). National Bureau of Economic Research.

While Covid-19 Outbreak Affects Economies and Societies; Exploring The Energy Demand in Turkey

Yıl 2021, Cilt: 11 Sayı: 2, 126 - 135, 30.12.2021
https://doi.org/10.36222/ejt.894463

Öz

The fear and panic environment created by the Coronavirus infectious disease 2019 (COVID-19), which affects the whole world, deeply affects the market mechanisms and causes energy supply and demand shocks. The common point of the predictions is that 2021 will pass with negative growth and increasing unemployment problem all over the world, with the economic reflections of the epidemic being felt seriously. In this study, Turkey in particular; a neural network and comparative regression model has been developed to analyze the effects on electricity and oil demand as part of COVID-19 economic measures. The purpose of this article is to contribute to the determination of Turkey's demand for electricity and oil during the epidemic and future by collecting information about direct and indirect factors, the events in the internal and external environments and their relationships of the pandemic. As a consequence, the elasticity of electricity and petroleum demand toward the population of the infected people is −0.323% and −0.397% respectively. The impact of COVID-19 on energy demand will be many times greater than the impact of the 2008 financial crisis on global energy demand. The mentioned findings show that the crisis deeply affected not only human health but also the global economy, clearly showed how the energy sector interacts with other factors of the economic structure.

Kaynakça

  • [1] Mofijur, M., Fattah, I. R., Alam, M. A., Islam, A. S., Ong, H. C., Rahman, S. A., Mahlia, T. M. I. (2020). Impact of COVID-19 on the social, economic, environmental and energy domains: Lessons learnt from a global pandemic. Sustainable production and consumption.
  • [2] Steffen, B., Egli, F., Pahle, M., Schmidt, T. S. (2020). Navigating the clean energy transition in the COVID-19 crisis. Joule, 4(6), 1137-1141.
  • [3] Rosenbloom, D., Markard, J. (2020). A COVID-19 recovery for climate.
  • [4] Hosseini, S. E. (2020). An outlook on the global development of renewable and sustainable energy at the time of Covid-19. Energy Research Social Science, 68, 101633.
  • [5] Rapaccini, M., Saccani, N., Kowalkowski, C., Paiola, M., Adrodegari, F. (2020). Navigating disruptive crises through service-led growth: The impact of COVID-19 on Italian manufacturing firms. Industrial Marketing Management, 88, 225-237.
  • [6] Donthu, N. Gustafsson, A. (2020). Effects of COVID-19 on business and research. Journal of business research, 117, 284.
  • [7] Elavarasan, R. M., Shafiullah, G. M., Raju, K., Mudgal, V., Arif, M. T., Jamal, T., ... Subramaniam, U. (2020). COVID-19: Impact analysis and recommendations for power sector operation. Applied energy, 279, 115739.
  • [8] Sovacool, B. K., Del Rio, D. F., Griffiths, S. (2020). Contextualizing the Covid-19 pandemic for a carbon-constrained world: Insights for sustainability transitions, energy justice, and research methodology. Energy Research Social Science, 68, 101701.
  • [9] Polemis, M., Soursou, S. (2020). Assessing the impact of the COVID-19 pandemic on the Greek energy firms: An event study analysis. Energy Research Letters, 1(3), 17238.
  • [10] Akrofi, M. M., Antwi, S. H. (2020). COVID-19 energy sector responses in Africa: a review of preliminary government interventions. Energy Research Social Science, 68, 101681.
  • [11] Wolff, S., Ladi, S. (2020). European Union Responses to the Covid-19 Pandemic: adaptability in times of Permanent Emergency. Journal of European Integration, 42(8), 1025-1040.
  • [12] Demirbilek, Y., Pehlivantürk, G., Özgüler, Z. Ö., Mese, E. A. (2020). COVID-19 outbreak control, example of ministry of health of Turkey. Turkish journal of medical sciences, 50(SI-1), 489-494.
  • [13] Abu-Rayash, A., Dincer, I. (2020). Analysis of the electricity demand trends amidst the COVID-19 coronavirus pandemic. Energy Research & Social Science, 68, 101682.
  • [14] Werth, A., Gravino, P., Prevedello, G. (2021). Impact analysis of COVID-19 responses on energy grid dynamics in Europe. Applied energy, 281, 116045.
  • [15] Irena April 2020, Global Renewables Outlook: Energy transformation 2050, Page:15, 34.
  • [16] Analytica, O. (2021). Climate change demands drastic action internationally. Expert Briefings.
  • [17] Selmi, R., Bouoiyour, J., Hammoudeh, S., Errami, Y., Wohar, M. E. (2021). The energy transition, Trump energy agenda and COVID-19. International Economics, 165, 140-153.
  • [18] Wärtsilä Corporation, news & press releases, 17 April 2020.
  • [19] Mike Fulwood, March 2020, $2 gas in Europe is here: who will blink first?, Oxfordenergy.
  • [20] NAB Group Economics, NAB MINERALS AND ENERGY OUTLOOK, JUNE 2020.
  • [21] Ember, Daily EU ETS carbon market price (Euros) , https://ember-climate.org/data/carbon-price-viewer/
  • [22] Erdin, C., Ozkaya, G. (2019). Turkey’s 2023 energy strategies and investment opportunities for renewable energy sources: site selection based on electre. Sustainability, 11(7), 2136.
  • [23] https://www.imf.org/en/Publications/WEO/Issues/2021/01/26/2021-world-economic-outlook-update
  • [24] Yilmaz, Musa, Kilic, Heybet, “Smart grid road map and challenges for Turkey” IET Digital Library, Microgrids for Rural Areas, page:389-420, (2020), ISBN:9781785619991
  • [25] https://www.intelligentliving.co/turkey-new-record-renewable-energy/
  • [26] Yılmaz, Musa, "Real Measure of a Transmission Line Data with Load Fore-cast Model for The Future". Balkan Journal of Electrical and Computer Engineering 6 / 2 (April 2018): 141-145.
  • [27] Deb, C., Zhang, F., Yang, J., Lee, S. E., Shah, K. W. (2017). A review on time series forecasting techniques for building energy consumption. Renewable and Sustainable Energy Reviews, 74, 902-924.
  • [28] Ghofrani, M., Suherli, A. (2017). Time series and renewable energy forecasting. Time Ser. Anal. Appl, 2017, 77-92.
  • [29] Berthou, T., Stabat, P., Salvazet, R., Marchio, D. (2014). Development and validation of a gray box model to predict thermal behavior of occupied office buildings. Energy and Buildings, 74, 91-100.
  • [30] Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A., & Hyndman, R. J. (2016). Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond.
  • [31] Rahmani, R., Yusof, R., Seyedmahmoudian, M., Mekhilef, S. (2013). Hybrid technique of ant colony and particle swarm optimization for short term wind energy forecasting. Journal of Wind Engineering and Industrial Aerodynamics, 123, 163-170.
  • [32] Kıran, M. S., Ozceylan, E., Gunduz, M., Paksoy, T. (2012). A novel hybrid approach based on particle swarm optimization and ant colony algorithm to forecast energy demand of Turkey. Energy conversion and management, 53(1), 75-83.
  • [33] AlRashidi, M. R., El-Naggar, K. M. (2010). Long term electric load forecasting based on particle swarm optimization. Applied Energy, 87(1), 320-326.
  • [34] Azadeh, A., Tarverdian, S. (2007). Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption. Energy policy, 35(10), 5229-5241.
  • [35] Viswavandya, M., Mohanty, A. (2018). Fuzzy Logic and ANFIS based Short Term Solar Energy Forecasting. Int. J. Futur. Revolut. Comput. Sci. Commun. Eng., 4, 631-636.
  • [36] Runge, J., Zmeureanu, R. (2019). Forecasting energy use in buildings using artificial neural networks: A review. Energies, 12(17), 3254.
  • [37] Suganthi, L., Samuel, A. A. (2012). Energy models for demand forecasting—A review. Renewable and sustainable energy reviews, 16(2), 1223-1240.
  • [38] Dumitru, C. D., Gligor, A. (2017). Daily average wind energy forecasting using artificial neural networks. Procedia Engineering, 181, 829-836.
  • [39] Galván, I. M., Valls, J. M., Cervantes, A., Aler, R. (2017). Multi-objective evolutionary optimization of prediction intervals for solar energy forecasting with neural networks. Information Sciences, 418, 363-382.
  • [40] Jasiński, T. (2019). Modeling electricity consumption using nighttime light images and artificial neural networks. Energy, 179, 831-842.
  • [41] Alanis, A. Y. (2018). Electricity prices forecasting using artificial neural networks. IEEE Latin America Transactions, 16(1), 105-111.
  • [42] Akarslan, E., Hocaoglu, F. O. (2018, March). Electricity demand forecasting of a micro grid using ANN. In 2018 9th International Renewable Energy Congress (IREC) (pp. 1-5). IEEE.
  • [43] Gajowniczek, K., Nafkha, R., Ząbkowski, T. (2017, September). Electricity peak demand classification with artificial neural networks. In 2017 Federated Conference on Computer Science and Information Systems (FedCSIS) (pp. 307-315). IEEE.
  • [44] Yasin Çodur, M., Ünal, A. (2019). An estimation of transport energy demand in Turkey via artificial neural networks. Promet-Traffic&Transportation, 31(2), 151-161.
  • [45] Martellotta, F., Ayr, U., Stefanizzi, P., Sacchetti, A., Riganti, G. (2017). On the use of artificial neural networks to model household energy consumptions. Energy Procedia, 126, 250-257.
  • [46] Han, T., Jiang, D., Zhao, Q., Wang, L., Yin, K. (2018). Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery. Transactions of the Institute of Measurement and Control, 40(8), 2681-2693.
  • [47] Metaxiotis, K., Kagiannas, A., Askounis, D., Psarras, J. (2003). Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher. Energy conversion and Management, 44(9), 1525-1534.
  • [48] Hippert, H. S., Pedreira, C. E., Souza, R. C. (2001). Neural networks for short-term load forecasting: A review and evaluation. IEEE Transactions on power systems, 16(1), 44-55.
  • [49] Jamei, M., Nisnevich, A., Wetchler, E., Sudat, S., Liu, E. (2017). Predicting all-cause risk of 30-day hospital readmission using artificial neural networks. PloS one, 12(7), e0181173.
  • [50] Saba, A. I., Elsheikh, A. H. (2020). Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks. Process safety and environmental protection, 141, 1-8.
  • [51] Hamadneh, N. N., Khan, W. A., Ashraf, W., Atawneh, S. H., Khan, I., Hamadneh, B. N. (2021). Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia. Comput. Mater. Contin, 66, 2787-2796.
  • [52] Sahoo, A., Singh, U. K., Kumar, M. H., Samantaray, S. (2021). Estimation of Flood in a River Basin Through Neural Networks: A Case Study. In Communication Software and Networks (pp. 755-763). Springer, Singapore.
  • [53] Pillai, S. R., Yadav, A., Vashishtha, V. K. (2021). Prediction of Wind Power Curve Based on Wind Speed and Direction Utilizing Artificial Neural Network. In Recent Advances in Mechanical Engineering (pp. 515-522). Springer, Singapore.
  • [54] Jena, P. R., Majhi, R., Kalli, R., Managi, S., Majhi, B. (2021). Impact of COVID-19 on GDP of major economies: Application of the artificial neural network forecaster. Economic Analysis and Policy, 69, 324-339.
  • [55] Ataei, A., Feghhi, T., Leventouri, T., Muhammad, D. W. (2021). Liver Cancer Risk Quantification through Artificial Neural Network. Bulletin of the American Physical Society.
  • [56] Noori, N., Kalin, L., Isik, S. (2020). Water quality prediction using SWAT-ANN coupled approach. Journal of Hydrology, 590, 125220.
  • [57] Li, B., Lee, Y., Yao, W., Lu, Y., Fan, X. (2020). Development and application of ANN model for property prediction of supercritical kerosene. Computers Fluids, 209, 104665.
  • [58] Verma, A., Prakash, S., Kumar, A. (2020). ANN‐based energy consumption prediction model up to 2050 for a residential building: Towards sustainable decision making. Environmental Progress Sustainable Energy, e13544.
  • [59] Fan, C., Ding, Y. (2019). Cooling load prediction and optimal operation of HVAC systems using a multiple nonlinear regression model. Energy and Buildings, 197, 7-17.
  • [60] Goldthorpe, J. H., Goldthorpe, O. F. J. H. (2000). On sociology: Numbers, narratives, and the integration of research and theory. Oxford University Press on Demand.
  • [61] Moosa, I. A. (2017). Econometrics as a con art: exposing the limitations and abuses of econometrics. Edward Elgar Publishing.
  • [62] https://tradingeconomics.com/turkey/gdp-growth-annual
  • [63] https://www.epdk.gov.tr/Detay/Icerik/3-0-104/aylik-sektor-raporu
  • [64] https://ytbsbilgi.teias.gov.tr/ytbsbilgi/frm_istatistikler.jsf
  • [65] https://covid19.who.int/region/euro/country/tr
  • [66] https://ourworldindata.org/coronavirus/country/turkey?country=~TUR
  • [67] https://tradingeconomics.com/turkey/manufacturing-pmi
  • [68] https://tradingeconomics.com/turkey/exports
  • [69] https://tradingeconomics.com/turkey/foreign-direct-investment
  • [70] https://tradingeconomics.com/turkey/industrial-production
  • [71] https://tr.investing.com/indices/ise-100-historical-data
  • [72] Satrovic, E. (2017). Financial development and human capital in Turkey: ARDL approach. Kapadokya Akademik Bakıs, 1(2), 1-15.
  • [73] Çetinkaya, Z., Erdal, E. (2019, September). Daily Food Demand Forecast with Artificial Neural Networks: Kırıkkale University Case. In 2019 4th International Conference on Computer Science and Engineering (UBMK) (pp. 1-6). IEEE.
  • [74] Norouzi, N., de Rubens, G. Z., Choubanpishehzafar, S., Enevoldsen, P. (2020). When pandemics impact economies and climate change: exploring the impacts of COVID-19 on oil and electricity demand in China. Energy Research & Social Science, 68, 101654.
  • [75] Bartik, A. W., Cullen, Z. B., Glaeser, E. L., Luca, M., Stanton, C. T. (2020). What jobs are being done at home during the COVID-19 crisis? Evidence from firm-level surveys (No. w27422). National Bureau of Economic Research.
Toplam 75 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

Hasan Huseyin Coban 0000-0002-5284-0568

Yayımlanma Tarihi 30 Aralık 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 11 Sayı: 2

Kaynak Göster

APA Coban, H. H. (2021). While Covid-19 Outbreak Affects Economies and Societies; Exploring The Energy Demand in Turkey. European Journal of Technique (EJT), 11(2), 126-135. https://doi.org/10.36222/ejt.894463

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