Artificial intelligence in energy industry: forecasting electricity consumption through cohort intelligence & adaptive neural fuzzy inference system

References

• Aladeemy, M., Tutun, S., & Khasawneh, M. T. (2017). A new hybrid approach for feature selection and support vector machine model selection based on self-adaptive cohort intelligence. Expert Systems with Applications, 88, 118–131. https://doi.org/10.1016/j.eswa.2017.06.030
   Web of Science ®Google Scholar
• Ardakani, F. J., & Ardehali, M. M. (2014). Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types. Energy, 65(1), 452–461. https://doi.org/10.1016/j.energy.2013.12.031
   Google Scholar
• Arora, S., & Taylor, J. W. (2018). Rule-based autoregressive moving average models for forecasting load on special days: A case study for France. European Journal of Operational Research, 266(1), 259–268. https://doi.org/10.1016/j.ejor.2017.08.056
   Web of Science ®Google Scholar
• Ayvaz, B., & Kusakci, A. O. (2017). Electricity consumption forecasting for Turkey with nonhomogeneous discrete grey model. Energy Sources, Part B: Economics, Planning, and Policy, 12(3), 260–267. https://doi.org/10.1080/15567249.2015.1089337
   Web of Science ®Google Scholar
• Azadeh, A., Ghaderi, S. F., & Sohrabkhani, S. (2008). Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors. Energy Conversion and Management, 49(8), 2272–2278. https://doi.org/10.1016/j.enconman.2008.01.035
   Web of Science ®Google Scholar
• Azadeh, A., Saberi, M., Ghaderi, S. F., Gitiforouz, A., & Ebrahimipour, V. (2008). Improved estimation of electricity demand function by integration of fuzzy system and data mining approach. Energy Conversion and Management, 49(8), 2165–2177. https://doi.org/10.1016/j.enconman.2008.02.021
   Web of Science ®Google Scholar
• Azadeh, A., Saberi, M., Gitiforouz, A., & Saberi, Z. (2009). A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation. Expert Systems with Applications, 36(8), 11108–11117. https://doi.org/10.1016/j.eswa.2009.02.081
   Web of Science ®Google Scholar
• Azadeh, A., Taghipour, M., Asadzadeh, S. M., & Abdollahi, M. (2014). Artificial immune simulation for improved forecasting of electricity consumption with random variations. International Journal of Electrical Power and Energy Systems, 55(1), 205–224. https://doi.org/10.1016/j.ijepes.2013.08.017
   Google Scholar
• Bessec, M., & Fouquau, J. (2018). Short-run electricity load forecasting with combinations of stationary wavelet transforms. European Journal of Operational Research, 264(1), 149–164. https://doi.org/10.1016/j.ejor.2017.05.037
   Web of Science ®Google Scholar
• Catalão, J. P. S., Pousinho, H. M. I., & Mendes, V. M. F. (2011). Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting. IEEE Transactions on Power Systems, 26(1), 137–144. https://doi.org/10.1109/TPWRS.2010.2049385
   Web of Science ®Google Scholar
• Crider, J. (2020). COVID-19 Bankrupts 19 energy (oil & gas) companies. CleanTechnica.https://cleantechnica.com/2020/08/05/covid-19-bankrupts-19-energy-oil-gas-companies. April/April/2021
   Google Scholar
• Duan, J., Qiu, X., Ma, W., Tian, X., & Shang, D. (2018). Electricity consumption forecasting scheme via improved LSSVM with maximum correntropy criterion. Entropy, 20(2), 112. https://doi.org/10.3390/e20020112
   Web of Science ®Google Scholar
• Duran Toksari, M. (2007). Ant colony optimization approach to estimate energy demand of Turkey. Energy Policy, 35(8), 3984–3990. https://doi.org/10.1016/j.enpol.2007.01.028
   Web of Science ®Google Scholar
• Ediger, V. Ş., & Akar, S. (2007). ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy, 35(3), 1701–1708. https://doi.org/10.1016/j.enpol.2006.05.009
   Web of Science ®Google Scholar
• Ediger, V. Ş., & Kentel, E. (1999). Renewable energy potential as an alternative to fossil fuels in Turkey. Energy Conversion and Management, 40(7), 743–755. https://doi.org/10.1016/S0196-8904(98)00122-8
   Web of Science ®Google Scholar
• Ediger, V. Ş., & Tatlidil, H. (2002). Forecasting the primary energy demand in Turkey and analysis of cyclic patterns. Energy Conversion and Management, 43(4), 473–487. https://doi.org/10.1016/S0196-8904(01)00033-4
   Web of Science ®Google Scholar
• Erdogdu, E. (2007). Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey. Energy Policy, 35(2), 1129–1146. https://doi.org/10.1016/j.enpol.2006.02.013
   Web of Science ®Google Scholar
• Hamzaçebi, C. (2007). Forecasting of Turkey’s net electricity energy consumption on sectoral bases. Energy Policy, 35(3), 2009–2016. https://doi.org/10.1016/j.enpol.2006.03.014
   Web of Science ®Google Scholar
• Hong, T., & Fan, S. (2016). Probabilistic electric load forecasting: A tutorial review. International Journal of Forecasting, 32(3), 914–938. https://doi.org/10.1016/j.ijforecast.2015.11.011
   Web of Science ®Google Scholar
• Hu, Y. C. (2017). Electricity consumption prediction using a neural-network-based grey forecasting approach. Journal of the Operational Research Society, 68(10), 1259–1264. https://doi.org/10.1057/s41274-016-0150-y
   Web of Science ®Google Scholar
• IEA. (2020). COVID-19 impact on electricity. https://www.iea.org/reports/covid-19-impact-on-electricity, April/28/2021
   Google Scholar
• (IEA). Global Energy Review. (2020). The impacts of the Covid-19 crisis on global energy demand and CO2 emissions. https://www.iea.org/reports/global-energy-review-2020. April/28/2021
   Google Scholar
• Jang, J. S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685. https://doi.org/10.1109/21.256541
   Web of Science ®Google Scholar
• Kaboli, S. H. A., Fallahpour, A., Selvaraj, J., & Rahim, N. A. (2017). Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming. Energy, 126(1), 144–164. https://doi.org/10.1016/j.energy.2017.03.009
   Google Scholar
• Kankal, M., & Uzlu, E. (2017). Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey. Neural Computing & Applications, 28(1), 737–747. https://doi.org/10.1007/s00521-016-2409-2
   Google Scholar
• Kavaklioglu, K. (2011). Modeling and prediction of Turkey’s electricity consumption using support vector regression. Applied Energy, 88(1), 368–375. https://doi.org/10.1016/j.apenergy.2010.07.021
   Web of Science ®Google Scholar
• Kavaklioglu, K. (2014). Robust electricity consumption modeling of Turkey using singular value decomposition. International Journal of Electrical Power and Energy Systems, 54(1), 268–276. https://doi.org/10.1016/j.ijepes.2013.07.020
   Google Scholar
• Kaytez, F. (2020). A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption. Energy, 197(1), 117200. https://doi.org/10.1016/j.energy.2020.117200
   Google Scholar
• Kaytez, F., Taplamacioglu, M. C., Cam, E., & Hardalac, F. (2015). Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. International Journal of Electrical Power and Energy Systems, 67(1), 431–438. https://doi.org/10.1016/j.ijepes.2014.12.036
   Google Scholar
• Kermanshahi, B., & Iwamiya, H. (2002). Up to year 2020 load forecasting using neural nets. International Journal of Electrical Power and Energy Systems, 24(9), 789–797. https://doi.org/10.1016/S0142-0615(01)00086-2
   Web of Science ®Google Scholar
• Kheirkhah, A., Azadeh, A., Saberi, M., Azaron, A., & Shakouri, H. (2013). Improved estimation of electricity demand function by using of artificial neural network, principal component analysis and data envelopment analysis. Computers & Industrial Engineering, 64(1), 425–441. https://doi.org/10.1016/j.cie.2012.09.017
   Web of Science ®Google Scholar
• Kulkarni, A. J., Baki, M. F., & Chaouch, B. A. (2016). Application of the cohort-intelligence optimization method to three selected combinatorial optimization problems. European Journal of Operational Research, 250(2), 427–447. https://doi.org/10.1016/j.ejor.2015.10.008
   Web of Science ®Google Scholar
• Kulkarni, A. J., Durugkar, I. P., & Kumar, M. (2013). Cohort intelligence: A self supervised learning behavior. Proceedings - 2013 IEEE international conference on systems, man, and cybernetics, SMC 2013, 1396–1400.
   Google Scholar
• Lee, Y. S., & Tong, L. I. (2011). Forecasting energy consumption using a grey model improved by incorporating genetic programming. Energy Conversion and Management, 52(1), 147–152. https://doi.org/10.1016/j.enconman.2010.06.053
   Web of Science ®Google Scholar
• Marcjasz, G., Uniejewski, B., & Weron, R. (2019). On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks. International Journal of Forecasting, 35(4), 1520–1532. https://doi.org/10.1016/j.ijforecast.2017.11.009
   Web of Science ®Google Scholar
• Marcjasz, G., Uniejewski, B., & Weron, R. (2020). Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts? International Journal of Forecasting, 36(2), 466–479. https://doi.org/10.1016/j.ijforecast.2019.07.002
   Web of Science ®Google Scholar
• Moghadam, R. G., Izadbakhsh, M. A., Yosefvand, F., & Shabanlou, S. (2019). Optimization of ANFIS network using firefly algorithm for simulating discharge coefficient of side orifices. Applied Water Science, 9(4), 1–12. https://doi.org/10.1007/s13201-019-0950-8
   Web of Science ®Google Scholar
• Moret, S., Babonneau, F., Bierlaire, M., & Maréchal, F. (2020). Decision support for strategic energy planning: A robust optimization framework. European Journal of Operational Research, 280(2), 539–554. https://doi.org/10.1016/j.ejor.2019.06.015
   Web of Science ®Google Scholar
• Oğcu, G., Demirel, O. F., & Zaim, S. (2012). Forecasting electricity consumption with neural networks and support vector regression. Procedia-Social and Behavioral Sciences, 58 1, 1576–1585.
   Google Scholar
• Ozturk, H. K., Ceylan, H., Canyurt, O. E., & Hepbasli, A. (2005). Electricity estimation using genetic algorithm approach: A case study of Turkey. Energy, 30(7), 1003–1012. https://doi.org/10.1016/j.energy.2004.08.008
   Web of Science ®Google Scholar
• Pousinho, H. M. I., Mendes, V. M. F., & Catalão, J. P. S. (2011). A hybrid pso-anfis approach for short-term wind power prediction in Portugal. Energy Conversion and Management, 52(1), 397–402. https://doi.org/10.1016/j.enconman.2010.07.015
   Web of Science ®Google Scholar
• Pousinho, H. M. I., Mendes, V. M. F., & Catalão, J. P. S. (2012). Short-term electricity prices forecasting in a competitive market by a hybrid PSO-ANFIS approach. International Journal of Electrical Power and Energy Systems, 39(1), 29–35. https://doi.org/10.1016/j.ijepes.2012.01.001
   Web of Science ®Google Scholar
• Salleh, M. N. M., & Hussain, K. (2016). A review of training methods of ANFIS for applications in business and economics. International Journal of U- and E- Service, Science and Technology, 9(7), 165–172. https://doi.org/10.14257/ijunesst.2016.9.7.17
   Google Scholar
• Sangrody, H., & Zhou, N. (2016). An initial study on load forecasting considering economic factors. IEEE power and energy society general meeting, 2016–November.
   Google Scholar
• Sangrody, H., Zhou, N., Tutun, S., Khorramdel, B., Motalleb, M., & Sarailoo, M. (2018). Long term forecasting using artificial intelligence methods. In 2018 IEEE power and energy conference at Illinois (PECI) (pp. 1–5). IEEE. https://doi.org/10.1109/peci.2018.8334980
   Google Scholar
• Simsek, S., Genc, O., Albizri, A., Dinc, S., & Gonen, B. (2020a). Artificial neural network incorporated decision support tool for point velocity prediction. Journal of Business Analytics, 3(1), 67–78. https://doi.org/10.1080/2573234X.2020.1751569
   Google Scholar
• Simsek, S., Gumus, M., Khalafalla, M., & Issa, T. B. (2020b). A hybrid data analytics approach for high-performance concrete compressive strength prediction. Journal of Business Analytics, 3(2), 158–168. https://doi.org/10.1080/2573234X.2020.1760741
   Google Scholar
• Sözen, A. (2009). Future projection of the energy dependency of Turkey using artificial neural network. Energy Policy, 37(11), 4827–4833. https://doi.org/10.1016/j.enpol.2009.06.040
   Web of Science ®Google Scholar
• Sözen, A., & Arcaklioglu, E. (2007). Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey. Energy Policy, 35(10), 4981–4992. https://doi.org/10.1016/j.enpol.2007.04.029
   Web of Science ®Google Scholar
• Steinbuks, J. (2019). Assessing the accuracy of electricity production forecasts in developing countries. International Journal of Forecasting, 35(3), 1175–1185. https://doi.org/10.1016/j.ijforecast.2019.04.009
   Web of Science ®Google Scholar
• Takagi, T., & Sugeno, M. Fuzzy identification of systems and its applications to modeling and control. (1985). IEEE Transactions on Systems, Man, and Cybernetics, 15(1), 116–132. SMC-. https://doi.org/10.1109/TSMC.1985.6313399
   Web of Science ®Google Scholar
• Toksari, M. D. (2009). Estimating the net electricity energy generation and demand using the ant colony optimization approach: case of Turkey. Energy Policy, 37(3), 1181–1187. https://doi.org/10.1016/j.enpol.2008.11.017
   Web of Science ®Google Scholar
• Toksari, M. D. (2016). A hybrid algorithm of ant colony optimization (ACO) and Iterated Local Search (ILS) for estimating electricity domestic consumption: case of Turkey. International Journal of Electrical Power and Energy Systems, 78(1), 776–782. https://doi.org/10.1016/j.ijepes.2015.12.032
   Google Scholar
• Tunç, M., Çamdali, Ü., & ParmaksizogluC.2006. 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, [ Turkey | World Energy Council. July 6, 2020, from], 34(1), 50–59. https://www.worldenergy.org/impact-communities/members/entry/turkey
   Web of Science ®Google Scholar
• Tutun, S., Chou, C. A., & Caniyilmaz, E. (2015). A new forecasting framework for volatile behavior in net electricity consumption: A case study in Turkey. Energy, 93(1), 2406–2422. https://doi.org/10.1016/j.energy.2015.10.064
   Google Scholar
• Ünler, A. (2008). Improvement of energy demand forecasts using swarm intelligence: the case of Turkey with projections to 2025. Energy Policy, 36(6), 1937–1944. https://doi.org/10.1016/j.enpol.2008.02.018
   Web of Science ®Google Scholar
• Xinqing, L., Tsoukalas, L. H., & Uhrig, R. E. (1996). Neurofuzzy approach for the anticipatory control of complex systems. IEEE international conference on fuzzy systems, 1, 587–593.
   Google Scholar
• Ziel, F., & Liu, B. (2016). Lasso estimation for GEFCom2014 probabilistic electric load forecasting. International Journal of Forecasting, 32(3), 1029–1037. https://doi.org/10.1016/j.ijforecast.2016.01.001
   Web of Science ®Google Scholar