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Assessing the correlation between the sustainable energy for all with doing a business by artificial neural network

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Abstract

In recent years, artificial intelligence-based solutions have become widespread in various fields and have been observed to produce important solutions to critical problems. In this context, it is aimed to assess and establish a direct correlation between energy production/consumption and establishing sustainable business models by using artificial intelligence models. Thus, artificial intelligence-based models have been developed by using parameters related to global energy consumption, doing business, and critical concepts of the relevant topics. The results show that the proposed artificial intelligent-based models reveal a significant correlation between doing business and energy. The outcome of the study could be used in the determination of country strategies in critical areas such as transportation, infrastructure, and education in the near and far future.

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References

  1. Ahmad T, Chen H, Shah WA (2019) Effective bulk energy consumption control and management for power utilities using artificial intelligence techniques under conventional and renewable energy resources. Int J Electric Power and Energy Syst. https://doi.org/10.1016/j.ijepes.2019.02.023

    Article  Google Scholar 

  2. Akinci TC (2011) Short term wind speed forecasting with ANN in Batman. Turkey Elektronika ir Elektrotechnika 107(1):41–45

    Google Scholar 

  3. Asongu SA, Odhiambo NM (2019) Challenges of doing business in africa: a systematic review. J Afr Bus 20(2):259–268. https://doi.org/10.1080/15228916.2019.1582294

    Article  Google Scholar 

  4. Bolandnazar E, Rohani A, Taki M (2020) Energy consumption forecasting in agriculture by artificial intelligence and mathematical models. Energy Sources, Part A: Recovery, Utilization, and Environ Effects 42(13):1618–1632. https://doi.org/10.1080/15567036.2019.1604872

    Article  Google Scholar 

  5. Bourdeau M, Zhai X, qiangNefzaouiGuoChatellier EXP (2019) Modeling and forecasting building energy consumption: a review of data-driven techniques. Sustain Cities Soc 48:101533. https://doi.org/10.1016/j.scs.2019.101533

    Article  Google Scholar 

  6. Chou J-S, Bui D-K (2014) Modeling heating and cooling loads by artificial intelligence for energy-efficient building design. Energy and Build 82(437):446. https://doi.org/10.1016/j.enbuild.2014.07.036

    Article  Google Scholar 

  7. Chou JS, Bui DK (2014) Modeling heating and cooling loads by artificial intelligence for energy-efficient building design. Energy and Buildings 82(2014):437–446. https://doi.org/10.1016/j.enbuild.2014.07.036

    Article  Google Scholar 

  8. Chui KT, Lytras MD, Visvizi A (2018) Energy sustainability in smart cities: artificial intelligence, smart monitoring, and optimization of energy consumption. Energies 11(11):1–20. https://doi.org/10.3390/en11112869

    Article  Google Scholar 

  9. Doing Business (2017): Equal Opportunity for All. (2016). In Doing Business 2017: Equal Opportunity for All. The World Bank. https://doi.org/10.1596/978-1-4648-0948-4

  10. Erdemir G, Akinci TC, Aslan Z (2021) Analyses and forecasting of solar energy potential by using ANN a case study of central Anatolia-Turkey. Fresenius Environmental Bulletin, pp. 11468–11468.

  11. Ertugrul ÖF (2016) Forecasting electricity load by a novel recurrent extreme learning machines approach. Int J Electrical Power Energy Syst 78:429–435

    Article  Google Scholar 

  12. Ertuğrul ÖF, Tağluk ME (2017) A fast feature selection approach based on extreme learning machine and coefficient of variation. Turk J Electr Eng Comput Sci 25(4):3409–3420

    Article  Google Scholar 

  13. Hou Z, Lian Z, Yao Y, Yuan X (2006) Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique. Appl Energy 83(9):1033–1046. https://doi.org/10.1016/j.apenergy.2005.08.006

    Article  Google Scholar 

  14. Huang G-B, Ding X, Zhou H (2010) Optimization method based extreme learning machine for classification. Neurocomputing 74(1–3):155–163

    Article  Google Scholar 

  15. Huang G-B, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2(2):107–122

    Article  Google Scholar 

  16. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  17. Huang G-B, Zhu Q-Y, & Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541), 2: 985–990.

  18. Kalogirou S (2007) Artificial intelligence in energy and renewable energy systems (1st ed.). Nova Publishers.

  19. Le LT, Nguyen H, Dou J, Zhou J (2019) A comparative study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in estimating the heating load of buildings’ energy efficiency for smart city planning. Appl Sci (Switzerland). https://doi.org/10.3390/app9132630

    Article  Google Scholar 

  20. Nishant R, Kennedy M, Corbett J (2020) Artificial intelligence for sustainability: challenges, opportunities, and a research agenda. Int J Inf Manage 53:102104. https://doi.org/10.1016/j.ijinfomgt.2020.102104

    Article  Google Scholar 

  21. Poulsen RT, Johnson H (2016) The logic of business vs. the logic of energy management practice: understanding the choices and effects of energy consumption monitoring systems in shipping companies. Journal of Cleaner Production 112:3785–3797. https://doi.org/10.1016/j.jclepro.2015.08.032

    Article  Google Scholar 

  22. Running DM, Ligon JB, Miskioglu I (1999) Relating attitudes to residential energy use. J Compos Mater 33(10):928–940

    Article  Google Scholar 

  23. Sadorsky P (2010) The impact of financial development on energy consumption in emerging economies. Energy Policy 38(5):2528–2535. https://doi.org/10.1016/j.enpol.2009.12.048

    Article  Google Scholar 

  24. Sekhar Roy S, Roy R, Balas VE (2018) Estimating heating load in buildings using multivariate adaptive regression splines, extreme learning machine, a hybrid model of MARS and ELM. Renew Sustain Energy Rev 82:4256–4268. https://doi.org/10.1016/j.rser.2017.05.249

    Article  Google Scholar 

  25. Şerban AC, Lytras MD (2020) Artificial intelligence for smart renewable energy sector in Europe—smart energy infrastructures for next generation smart cities. IEEE Access 8:77364–77377. https://doi.org/10.1109/ACCESS.2020.2990123

    Article  Google Scholar 

  26. Sholahudin S, Han H (2016) Simplified dynamic neural network model to predict heating load of a building using Taguchi method. Energy 115:1672–1678. https://doi.org/10.1016/j.energy.2016.03.057

    Article  Google Scholar 

  27. Tabor DP, Roch LM, Saikin SK, Kreisbeck C, Sheberla D, Montoya JH, Dwaraknath S, Aykol M, Ortiz C, Tribukait H, Amador-Bedolla C, Brabec CJ, Maruyama B, Persson KA, Aspuru-Guzik A (2018) Accelerating the discovery of materials for clean energy in the era of smart automation. Nat Rev Mater 3(5):5–20. https://doi.org/10.1038/s41578-018-0005-z

    Article  Google Scholar 

  28. Tantau A, Şanta A-MI (2021) New energy policy directions in the european union developing the concept of smart cities. Smart Cities 4(1):241–252. https://doi.org/10.3390/smartcities4010015

    Article  Google Scholar 

  29. Ullah Z, Al-Turjman F, Mostarda L, Gagliardi R (2020) Applications of Artificial Intelligence and Machine learning in smart cities. Comput Commun 154:313–323. https://doi.org/10.1016/j.comcom.2020.02.069

    Article  Google Scholar 

  30. Wahid F, Ismail LH, Ghazali R, Aamir M (2019) An efficient artificial intelligence hybrid approach for energy management in intelligent buildings. KSII Trans Internet Inf Syst 13(12):5904–5927. https://doi.org/10.3837/tiis.2019.12.007

    Article  Google Scholar 

  31. Wang Z, Srinivasan RS (2017) A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. Renew Sustain Energy Rev 7:796–808. https://doi.org/10.1016/j.rser.2016.10.079

    Article  Google Scholar 

  32. Wang Z, Wang Y, Srinivasan RS (2018) A novel ensemble learning approach to support building energy use prediction. Energy and Build 159:109–122

    Article  Google Scholar 

  33. Zahraee SM, Khalaji Assadi M, Saidur R (2016) Application of artificial intelligence methods for hybrid energy system optimization. Renew Sustain Energy Rev 66:617–630. https://doi.org/10.1016/j.rser.2016.08.028

    Article  Google Scholar 

  34. Zaman K (2018) The impact of hydro-biofuel-wind energy consumption on environmental cost of doing business in a panel of BRICS countries: evidence from three-stage least squares estimator. Environ Sci Pollut Res 25(5):4479–4490. https://doi.org/10.1007/s11356-017-0797-1

    Article  Google Scholar 

  35. Zhang J, Haghighat F (2010) Development of Artificial Neural Network based heat convection algorithm for thermal simulation of large rectangular cross-sectional area Earth-to-Air Heat Exchangers. Energy and Build 42(4):435–440. https://doi.org/10.1016/j.enbuild.2009.10.011

    Article  Google Scholar 

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  1. Department of International Relations, Ankara Sosyal Bilimler University, Ankara, Turkey

    İdris Demir

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Demir, İ. Assessing the correlation between the sustainable energy for all with doing a business by artificial neural network. Neural Comput & Applic 34, 22087–22097 (2022). https://doi.org/10.1007/s00521-022-07638-y

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