Panel quantile regression neural network for electricity consumption forecasting in China: a new framework

References

• Abdoli, G., Y. Farahani, and S. Dastan. 2015. Electricity consumption and economic growth in opec countries: A cointegrated panel analysis. OPEC Energy Review 39 (1):1–16. doi:10.1111/opec.12038.
   Web of Science ®Google Scholar
• Alexander, M., M. Harding, and C. Lamarche. 2011. Quantile regression for time-series cross section data. International Journal of Statistics and Management System 6:47–72.
   Google Scholar
• Allard, A., J. Takman, G. Uddin, and A. Ahmed. 2018. The n-shaped environmental kuznets curve: An empirical evaluation using a panel quantile regression approach. Environmental Science and Pollution Research 25 (6):5848–61. doi:10.1007/s11356-017-0907-0.
   PubMed Web of Science ®Google Scholar
• Arellano, M., and S. Bonhomme. 2016. Nonlinear panel data estimation via quantile regressions. Econometrics Journal 19 (3):61–94. doi:10.1111/ectj.12062.
   Web of Science ®Google Scholar
• Azadeh, A., S. Ghaderi, and S. Sohrabkhani. 2007. Forecasting electrical consumption by integration of neural network, time series and anova. Applied Mathematics and Computation 186 (2):1753–61. doi:10.1016/j.amc.2006.08.094.
   Web of Science ®Google Scholar
• Azadeh, A., S. Ghaderi, and S. Sohrabkhani. 2008. A simulated-based neural network algorithm for forecasting electrical energy consumption in iran. Energy Policy 36 (7):2637–44. doi:10.1016/j.enpol.2008.02.035.
   Web of Science ®Google Scholar
• Bassamzadeh, N., and R. Ghanem. 2017. Multiscale stochastic prediction of electricity demand in smart grids using bayesian networks. Applied Energy 193:369–80. doi:10.1016/j.apenergy.2017.01.017.
   Web of Science ®Google Scholar
• Cabral, J. A., L. Legey, and M. Cabral. 2017. Electricity consumption forecasting in brazil: A spatial econometrics approach. Energy 126:124–31. doi:10.1016/j.energy.2017.03.005.
   Web of Science ®Google Scholar
• Canay, I. A. 2011. A simple approach to quantile regression for panel data. The Econometrics Journal 14 (3):368–86. doi:10.1111/j.1368-423X.2011.00349.x.
   Web of Science ®Google Scholar
• Cannon, A. 2011. Quantile regression neural networks: Implementation in r and application to precipitation downscaling. Computational Geosciences 37 (9):1277–84. doi:10.1016/j.cageo.2010.07.005.
   Web of Science ®Google Scholar
• Cannon, A. 2018. Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall. Stochastic Environmental Research and Risk Assessment 32 (11):3207–35. doi:10.1007/s00477-018-1573-6.
   Web of Science ®Google Scholar
• Catalao, J., S. Mariano, V. Mendes, and L. Ferreira. 2007. Short-term electricity prices forecasting in a competitive market: A neural network approach. Electric Power Systems Research 77 (10):1297–304. doi:10.1016/j.epsr.2006.09.022.
   Web of Science ®Google Scholar
• Chasset, P. O. (2015). R Package: Grnn. https://cran.r-project.org/package=grnn
   Google Scholar
• Chaudhry, A. A. 2016. A panel data analysis of electricity demand in the pakistani industrial sector. Energy Sources, Part B: Economics, Planning, and Policy 11 (1):73–79. doi:10.1080/15567249.2011.576376.
   Web of Science ®Google Scholar
• Chen, S., Z. Xie, and Y. Liao. 2018. Energy consumption promotes economic growth or economic growth causes energy use in china? a panel data analysis. Empirical Economics 55 (3):1019–43. doi:10.1007/s00181-017-1319-1.
   Web of Science ®Google Scholar
• Clevert, D., T. Unterthiner, and S. Hochreiter (2016). Fast and accurate deep network learning by exponential linear units. In Conference: ICLR, San Juan, Puerto Rico, May 2-4, 2016.
   Google Scholar
• Dauphin, Y. N., R. Pascanu, C. Gulcehre, K. Cho, S. Ganguli, and Y. Bengio. 2014. Identifying and attacking the saddle point problem in high-dimensional nonconvex optimization. arXiv Preprint arXiv 1406.2572v1, 1–14.
   Google Scholar
• Ding, S., K. Hipel, and Y. Dang. 2018. Forecasting china’s electricity consumption using a new grey prediction model. Energy 149:314–28. doi:10.1016/j.energy.2018.01.169.
   Web of Science ®Google Scholar
• Ediger, V., and S. Akar. 2007. Arima forecasting of primary energy demand by fuel in turkey. Energy Policy 35 (3):1701–08. doi:10.1016/j.enpol.2006.05.009.
   Web of Science ®Google Scholar
• Feng, Y., R. Li, A. Sudjianto, and Y. Zhang. 2010. Robust neural network with applications to credit portfolio data analysis. Statistics and Its Interface 3 (4):437–44. doi:10.4310/SII.2010.v3.n4.a2.
   PubMed Web of Science ®Google Scholar
• Fritsch, S., F. Guenther, M. Wright, M. Suling, and S. Mueller (2019). R Package: Neuralnet. https://github.com/bips-hb/neuralnet
   Google Scholar
• Galvao, A. F. 2011. Quantile regression for dynamic panel data with fixed effects. Journal of Econometrics 164 (1):142–57. doi:10.1016/j.jeconom.2011.02.016.
   Web of Science ®Google Scholar
• Galvao, A. F. and L. Wang. 2015. Efficient minimum distance estimator for quantile regression fixed effects panel data. Journal of Multivariate Analysis 133: 1–26. doi:10.1016/j.jmva.2014.08.007.
   Google Scholar
• Galvao, A. F., C. Lamarche, and L. Lima. 2013. Estimation of censored quantile regression for panel data with fixed effects. Journal of the American Statistical Association 108 (503):1075–89. doi:10.1080/01621459.2013.818002.
   Web of Science ®Google Scholar
• Galvao, A. F., and G. Montes-Rojas. 2010. Penalized quantile regression for dynamic panel data. Journal of Statistical Planning and Inference 140 (11):3476–97. doi:10.1016/j.jspi.2010.05.008.
   Web of Science ®Google Scholar
• He, Y., and H. Li. 2018. Probability density forecasting of wind power using quantile regression neural network and kernel density estimation. Energy Conversion and Management 164:374–84. doi:10.1016/j.enconman.2018.03.010.
   Web of Science ®Google Scholar
• He, Y., Q. Xu, J. Wan, and S. Yang. 2016. Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function. Energy 114:498–512. doi:10.1016/j.energy.2016.08.023.
   Web of Science ®Google Scholar
• He, Y., Y. Qin, S. Wang, X. Wang, and C. Wang. 2019. Electricity consumption probability density forecasting method based on lasso-quantile regression neural network. Applied Energy 233-234:565–75. doi:10.1016/j.apenergy.2018.10.061.
   Web of Science ®Google Scholar
• Hsu, C. C., and C. Chen. 2003. Regional load forecasting in taiwan-applications of artificial neural networks. Energy Conversion and Management 44 (12):1941–49. doi:10.1016/S0196-8904(02)00225-X.
   Web of Science ®Google Scholar
• Hussain, A., M. Rahman, and J. Memon. 2016. Forecasting electricity consumption in pakistan: The way forward. Energy Policy 90:73–80. doi:10.1016/j.enpol.2015.11.028.
   Web of Science ®Google Scholar
• Karanifil, F., and Y. Li. 2015. Electricity consumption and economic growth: Exploring panel-specific differences. Energy Policy 82:264–77. doi:10.1016/j.enpol.2014.12.001.
   Web of Science ®Google Scholar
• Kaytez, F., M. Taplamacioglu, E. Cam, and F. Hardalac. 2015. Forecasting electricity consumption: A comparison of regression analysis, neural networks and least square support vector machines. Electrical Power and Energy Systems 67:431–38. doi:10.1016/j.ijepes.2014.12.036.
   Web of Science ®Google Scholar
• Koenker, R. 2004. Quantile regression for longitudinal data. Journal of Multivariate Analysis 91 (1):74–89. doi:10.1016/j.jmva.2004.05.006.
   Web of Science ®Google Scholar
• Koenker, R. 2005. Quantile Regression. Cambridge: Cambridge University Press.
   Google Scholar
• Koenker, R., and G. Bassett. 1978. Regression quantiles. Econometrica 46 (1):33–50. doi:10.2307/1913643.
   Web of Science ®Google Scholar
• Kula, F. 2014. The long-run relationship between renewable electricity consumption and gdp: Evidence from panel data. Energy Sources, Part B: Economics, Planning, and Policy 9 (2):156–60. doi:10.1080/15567249.2010.481655.
   Web of Science ®Google Scholar
• Lamarche, C. 2010. Robust penalized quantile regression estimation for panel data. Journal of Econometrics 157 (2):396–408. doi:10.1016/j.jeconom.2010.03.042.
   Web of Science ®Google Scholar
• Latif, E. 2015. A panel data analysis of the demand for electricity in canada. Economic Paper 34 (3):192–205. doi:10.1111/1759-3441.12103.
   Web of Science ®Google Scholar
• Meer, D., J. Widen, and J. Munkhammar. 2018. Review on probabilistic forecasting of photovoltaic power production and electricity consumption. Renewable and Sustainable Energy Reviews 81:1484–512. doi:10.1016/j.rser.2017.05.212.
   Web of Science ®Google Scholar
• Meng, M., and D. Niu. 2011. Annual electricity consumption analysis and forecasting of china based on few observations methods. Energy Conversion and Management 52 (2):953–57. doi:10.1016/j.enconman.2010.08.023.
   Web of Science ®Google Scholar
• Meyer, D., E. Dimitradou, K. Hornik, A. Weingessel, F. Leisch, C. Chang, and C. Lin (2019). R Package: E1071. https://cran.r-project.org/package=e1071
   Google Scholar
• Mohamed, N., M. Ahmad, Z. Ismail, and Suhartono. 2010. Double seasonal arima model for forecasting load demand. Matematika 26:217–31.
   Google Scholar
• Mosteller, F. 1946. On some useful “inefficient” statistics. The Annals of Mathematical Statistics 17 (4):377–408. doi:10.1214/aoms/1177730881.
   Google Scholar
• Mouselimis, L., and A. Gosso (2020). R Package: ElmNNRcpp. https://cran.r-project.org/package=elmNNRcpp
   Google Scholar
• Osman, M., G. Gachino, and A. Hoque. 2016. Electricity consumption and economic growth in the gcc countries: Panel data analysis. Energy Policy 98:318–27. doi:10.1016/j.enpol.2016.07.050.
   Web of Science ®Google Scholar
• Panklib, K., C. Prakasvudhisarn, and D. Khummongkol. 2015. Electricity consumption forecasting in thailand using an artificial neural network and multiple linear regression. Energy Sources, Part B: Economics, Planning, and Policy 10 (4):427–34. doi:10.1080/15567249.2011.559520.
   Web of Science ®Google Scholar
• Pappasa, S., L. Ekonomoub, D. Karamousantasc, G. Chatzarakisb, S. Katsikasd, and P. Liatsis. 2008. Electricity demand loads modeling using autoregressive moving average (arma) models. Energy 33 (9):1353–60. doi:10.1016/j.energy.2008.05.008.
   Web of Science ®Google Scholar
• Ruder, S. 2017. An overview of gradient descent optimization algorithms. arXiv Preprint arXiv 1609.04747v2, 1–14.
   Google Scholar
• Sarkodie, S. A. 2017. Estimating ghana’s electricity consumption by 2030: An arima forecast. Energy Sources, Part B: Economics, Planning, and Policy 12 (10):936–44. doi:10.1080/15567249.2017.1327993.
   Web of Science ®Google Scholar
• Shao, Z., F. Gao, Q. Zhang, and S. Yang. 2015. Multivariate statistical and similarity measure based semiparametric modeling of the probability distribution: A novel approach to the case study of mid-long term electricity consumption forecasting in China. Applied Energy 156:502–18. doi:10.1016/j.apenergy.2015.07.037.
   Web of Science ®Google Scholar
• Taylor, J. W. 2000. A quantile regression neural network approach to estimating the conditional density of multiperiod returns. Journal of Forecasting 19 (4):299–311. doi:10.1002/1099-131X(200007)19:4<299::AID-FOR775>3.0.CO;2-V.
   Web of Science ®Google Scholar
• Taylor, J. W. 2010. Triple seasonal methods for short-term electricity demand forecasting. European Journal of Operational Research 204 (1):139–52. doi:10.1016/j.ejor.2009.10.003.
   Web of Science ®Google Scholar
• Torrini, F. C., R. Souza, F. Oliveira, and J. Pessanha. 2016. Long term electricity consumption forecast in brazil: A fuzzy logic approach. Socio-economic Planning Sciences 54:18–27. doi:10.1016/j.seps.2015.12.002.
   Web of Science ®Google Scholar
• Wang, S., J. Zeng, and X. Liu. 2019. Examining the multiple impacts of technological progress on co2 emissions in china: A panel quantile regression approach. Renewable and Sustainable Energy Reviews 103:140–50. doi:10.1016/j.rser.2018.12.046.
   Web of Science ®Google Scholar
• Wang, Y., Q. Zhang, and C. Li. 2019. The contribution of non-fossil power generation to reduction of electricity-related CO2 emissions: A panel quintile regression analysis. Journal of Cleaner Production 207:531–41. doi:10.1016/j.jclepro.2018.10.009.
   Web of Science ®Google Scholar
• Xu, Q., K. Deng, C. Jiang, F. Sun, and X. Huang. 2017. Composite quantile regression neural network with applications. Expert Systems with Applications 76:129–39. doi:10.1016/j.eswa.2017.01.054.
   Web of Science ®Google Scholar
• Xu, Q., X. Liu, C. Jiang, and K. Yu. 2016. Quantile autoregression neural network model with applications to evaluating value at risk. Applied Soft Computing 49:1–12. doi:10.1016/j.asoc.2016.08.003.
   Web of Science ®Google Scholar
• Yang, Y., J. Che, C. Deng, and L. Li. 2019. Sequential grid approach based support vector regression for short-term electric load forecasting. Applied Energy 238:1010–21. doi:10.1016/j.apenergy.2019.01.127.
   Web of Science ®Google Scholar
• Zhang, X., J. Wang, and K. Zhang. 2017. Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by cuckoo search algorithm. Electric Power Systems Research 146:270–85. doi:10.1016/j.epsr.2017.01.035.
   Web of Science ®Google Scholar
• Zou, H., and M. Yuan. 2008. Composite quantile regression and the oracle model selection theory. Annals of Statistics 36 (3):1108–26. doi:10.1214/07-AOS507.
   Web of Science ®Google Scholar