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Forecasting the short-term demand for electricity: Do neural networks stand a better chance?

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  1. Ohtsuka, Yoshihiro & Oga, Takashi & Kakamu, Kazuhiko, 2010. "Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2721-2735, November.
  2. Gogolev, Stepan & Ozhegov, Evgeniy, 2023. "Asymmetric loss function in product-level sales forecasting: An empirical comparison," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 70, pages 109-121.
  3. Do, Linh Phuong Catherine & Lin, Kuan-Heng & Molnár, Peter, 2016. "Electricity consumption modelling: A case of Germany," Economic Modelling, Elsevier, vol. 55(C), pages 92-101.
  4. Xiao, Liye & Shao, Wei & Wang, Chen & Zhang, Kequan & Lu, Haiyan, 2016. "Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting," Applied Energy, Elsevier, vol. 180(C), pages 213-233.
  5. Hu, Junjie & López Cabrera, Brenda & Melzer, Awdesch, 2021. "Advanced statistical learning on short term load process forecasting," IRTG 1792 Discussion Papers 2021-020, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  6. Bessec, Marie & Fouquau, Julien, 2018. "Short-run electricity load forecasting with combinations of stationary wavelet transforms," European Journal of Operational Research, Elsevier, vol. 264(1), pages 149-164.
  7. Soares, Lacir Jorge & Souza, Leonardo Rocha, 2006. "Forecasting electricity demand using generalized long memory," International Journal of Forecasting, Elsevier, vol. 22(1), pages 17-28.
  8. Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
  9. Taylor, James W., 2006. "Density forecasting for the efficient balancing of the generation and consumption of electricity," International Journal of Forecasting, Elsevier, vol. 22(4), pages 707-724.
  10. Jaume Rosselló Nadal & Mohcine Bakhat, 2009. "A new approach to estimating tourism-induced electricity consumption," CRE Working Papers (Documents de treball del CRE) 2009/6, Centre de Recerca Econòmica (UIB ·"Sa Nostra").
  11. Carolina Deina & João Lucas Ferreira dos Santos & Lucas Henrique Biuk & Mauro Lizot & Attilio Converti & Hugo Valadares Siqueira & Flavio Trojan, 2023. "Forecasting Electricity Demand by Neural Networks and Definition of Inputs by Multi-Criteria Analysis," Energies, MDPI, vol. 16(4), pages 1-24, February.
  12. Nikos Sakkas & Sofia Yfanti & Pooja Shah & Nikitas Sakkas & Christina Chaniotakis & Costas Daskalakis & Eduard Barbu & Marharyta Domnich, 2023. "Explainable Approaches for Forecasting Building Electricity Consumption," Energies, MDPI, vol. 16(20), pages 1-20, October.
  13. Chan, Kam Fong & Gray, Philip & van Campen, Bart, 2008. "A new approach to characterizing and forecasting electricity price volatility," International Journal of Forecasting, Elsevier, vol. 24(4), pages 728-743.
  14. Bennett, Christopher J. & Stewart, Rodney A. & Lu, Jun Wei, 2014. "Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system," Energy, Elsevier, vol. 67(C), pages 200-212.
  15. Taylor, James W. & Snyder, Ralph D., 2012. "Forecasting intraday time series with multiple seasonal cycles using parsimonious seasonal exponential smoothing," Omega, Elsevier, vol. 40(6), pages 748-757.
  16. Rocha Souza, Leonardo & Jorge Soares, Lacir, 2007. "Electricity rationing and public response," Energy Economics, Elsevier, vol. 29(2), pages 296-311, March.
  17. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
  18. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601, December.
  19. Mak Kaboudan, 2006. "Computational Forecasting of Wavelet-converted Monthly Sunspot Numbers," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(9), pages 925-941.
  20. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.
  21. Mostafa, Mohamed M. & Nataraajan, Rajan, 2009. "A neuro-computational intelligence analysis of the ecological footprint of nations," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3516-3531, July.
  22. Martin Madera & Dusan Marcek, 2023. "Intelligence in Finance and Economics for Predicting High-Frequency Data," Mathematics, MDPI, vol. 11(2), pages 1-15, January.
  23. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
  24. repec:qut:auncer:wp103 is not listed on IDEAS
  25. de Menezes, Lilian M. & Nikolaev, Nikolay Y., 2006. "Forecasting with genetically programmed polynomial neural networks," International Journal of Forecasting, Elsevier, vol. 22(2), pages 249-265.
  26. Taylor, James W., 2008. "An evaluation of methods for very short-term load forecasting using minute-by-minute British data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 645-658.
  27. Cancelo, José Ramón & Grafe, Rosmarie, 2007. "Forecasting from one day to one week ahead for the Spanish system operator," DES - Working Papers. Statistics and Econometrics. WS ws078418, Universidad Carlos III de Madrid. Departamento de Estadística.
  28. Hong, Wei-Chiang, 2010. "Application of chaotic ant swarm optimization in electric load forecasting," Energy Policy, Elsevier, vol. 38(10), pages 5830-5839, October.
  29. Clements, A.E. & Hurn, A.S. & Li, Z., 2016. "Forecasting day-ahead electricity load using a multiple equation time series approach," European Journal of Operational Research, Elsevier, vol. 251(2), pages 522-530.
  30. Gulay, Emrah & Duru, Okan, 2020. "Hybrid modeling in the predictive analytics of energy systems and prices," Applied Energy, Elsevier, vol. 268(C).
  31. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
  32. Elamin, Niematallah & Fukushige, Mototsugu, 2018. "Modeling and forecasting hourly electricity demand by SARIMAX with interactions," Energy, Elsevier, vol. 165(PB), pages 257-268.
  33. Chu, Ching-Wu & Zhang, Guoqiang Peter, 2003. "A comparative study of linear and nonlinear models for aggregate retail sales forecasting," International Journal of Production Economics, Elsevier, vol. 86(3), pages 217-231, December.
  34. Christopher Bennett & Rodney A. Stewart & Junwei Lu, 2014. "Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks," Energies, MDPI, vol. 7(5), pages 1-23, April.
  35. Potocnik, Primoz & Thaler, Marko & Govekar, Edvard & Grabec, Igor & Poredos, Alojz, 2007. "Forecasting risks of natural gas consumption in Slovenia," Energy Policy, Elsevier, vol. 35(8), pages 4271-4282, August.
  36. Cancelo, José Ramón & Espasa, Antoni & Grafe, Rosmarie, 2008. "Forecasting the electricity load from one day to one week ahead for the Spanish system operator," International Journal of Forecasting, Elsevier, vol. 24(4), pages 588-602.
  37. Chatum Sankalpa & Somsak Kittipiyakul & Seksan Laitrakun, 2022. "Forecasting Short-Term Electricity Load Using Validated Ensemble Learning," Energies, MDPI, vol. 15(22), pages 1-30, November.
  38. Heravi, Saeed & Osborn, Denise R. & Birchenhall, C. R., 2004. "Linear versus neural network forecasts for European industrial production series," International Journal of Forecasting, Elsevier, vol. 20(3), pages 435-446.
  39. Qi, Min & Yang, Sha, 2003. "Forecasting consumer credit card adoption: what can we learn about the utility function?," International Journal of Forecasting, Elsevier, vol. 19(1), pages 71-85.
  40. Kim, Myung Suk, 2013. "Modeling special-day effects for forecasting intraday electricity demand," European Journal of Operational Research, Elsevier, vol. 230(1), pages 170-180.
  41. Pao, Hsiao-Tien, 2006. "Comparing linear and nonlinear forecasts for Taiwan's electricity consumption," Energy, Elsevier, vol. 31(12), pages 2129-2141.
  42. Conejo, Antonio J. & Contreras, Javier & Espinola, Rosa & Plazas, Miguel A., 2005. "Forecasting electricity prices for a day-ahead pool-based electric energy market," International Journal of Forecasting, Elsevier, vol. 21(3), pages 435-462.
  43. Julien Boelaert, 2013. "A Neural Network Demand System," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00917810, HAL.
  44. J W Taylor, 2003. "Short-term electricity demand forecasting using double seasonal exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 799-805, August.
  45. Bakhat, Mohcine & Rosselló, Jaume, 2011. "Estimation of tourism-induced electricity consumption: The case study of Balearics Islands, Spain," Energy Economics, Elsevier, vol. 33(3), pages 437-444, May.
  46. Jose Juan Caceres-Hernandez & Gloria Martin-Rodriguez & Jonay Hernandez-Martin, 2022. "A proposal for measuring and comparing seasonal variations in hourly economic time series," Empirical Economics, Springer, vol. 62(4), pages 1995-2021, April.
  47. Souza, Leonardo Rocha & Soares, Lacir Jorge, 2003. "Forecasting electricity load demand: analysis of the 2001 rationing period in Brazil," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 491, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
  48. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
  49. Soares, Lacir J. & Medeiros, Marcelo C., 2008. "Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 630-644.
  50. Wei-Chiang Hong & Yucheng Dong & Chien-Yuan Lai & Li-Yueh Chen & Shih-Yung Wei, 2011. "SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting," Energies, MDPI, vol. 4(6), pages 1-18, June.
  51. Óscar Trull & J. Carlos García-Díaz & Alicia Troncoso, 2019. "Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter," Energies, MDPI, vol. 12(6), pages 1-16, March.
  52. Julien Boelaert, 2013. "A Neural Network Demand System," Documents de travail du Centre d'Economie de la Sorbonne 13081, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  53. Wang, Chi-hsiang & Grozev, George & Seo, Seongwon, 2012. "Decomposition and statistical analysis for regional electricity demand forecasting," Energy, Elsevier, vol. 41(1), pages 313-325.
  54. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2016. "Oil price forecasting using gene expression programming and artificial neural networks," Economic Modelling, Elsevier, vol. 54(C), pages 40-53.
  55. Azadeh, A. & Tarverdian, S., 2007. "Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption," Energy Policy, Elsevier, vol. 35(10), pages 5229-5241, October.
  56. Sarat Chandra Nayak & Bijan Bihari Misra, 2019. "A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-34, December.
  57. Arash Jamalmanesh & Mahdi Khodaparast Mashhadi & Ahmad Seifi & Mohammad Ali Falahi, 2018. "Prediction of Hydropower Energy Price Using G mes-Maravall Seasonal Model," International Journal of Energy Economics and Policy, Econjournals, vol. 8(2), pages 81-88.
  58. Lacir J. Soares & Marcelo Cunha Medeiros, 2005. "Modelling and forecasting short-term electricity load: a two step methodology," Textos para discussão 495, Department of Economics PUC-Rio (Brazil).
  59. Herrera, Gabriel Paes & Constantino, Michel & Tabak, Benjamin Miranda & Pistori, Hemerson & Su, Jen-Je & Naranpanawa, Athula, 2019. "Long-term forecast of energy commodities price using machine learning," Energy, Elsevier, vol. 179(C), pages 214-221.
  60. Kamal Chapagain & Somsak Kittipiyakul & Pisut Kulthanavit, 2020. "Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand," Energies, MDPI, vol. 13(10), pages 1-29, May.
  61. Hassani, Hossein & Webster, Allan & Silva, Emmanuel Sirimal & Heravi, Saeed, 2015. "Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis," Tourism Management, Elsevier, vol. 46(C), pages 322-335.
  62. Martin-Rodriguez, Gloria & Caceres-Hernandez, Jose Juan, 2005. "Modelling the hourly Spanish electricity demand," Economic Modelling, Elsevier, vol. 22(3), pages 551-569, May.
  63. Taylor, James W., 2010. "Triple seasonal methods for short-term electricity demand forecasting," European Journal of Operational Research, Elsevier, vol. 204(1), pages 139-152, July.
  64. Walker, Shalika & Bergkamp, Vince & Yang, Dujuan & van Goch, T.A.J. & Katic, Katarina & Zeiler, Wim, 2021. "Improving energy self-sufficiency of a renovated residential neighborhood with heat pumps by analyzing smart meter data," Energy, Elsevier, vol. 229(C).
  65. William Irungu Nganga & Julien Chevallier & Simon Wagura Ndiritu, 2018. "Regime changes and fiscal sustainability in Kenya with comparative nonlinear Granger causalities across East-African countries," Working Papers halshs-01941226, HAL.
  66. Cruz E. Borges & Yoseba K. Penya & Iván Fernández & Juan Prieto & Oscar Bretos, 2013. "Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings," Energies, MDPI, vol. 6(4), pages 1-20, April.
  67. Pedregal, Diego J. & Young, Peter C., 2006. "Modulated cycles, an approach to modelling periodic components from rapidly sampled data," International Journal of Forecasting, Elsevier, vol. 22(1), pages 181-194.
  68. Powell, Kody M. & Sriprasad, Akshay & Cole, Wesley J. & Edgar, Thomas F., 2014. "Heating, cooling, and electrical load forecasting for a large-scale district energy system," Energy, Elsevier, vol. 74(C), pages 877-885.
  69. Mat Daut, Mohammad Azhar & Hassan, Mohammad Yusri & Abdullah, Hayati & Rahman, Hasimah Abdul & Abdullah, Md Pauzi & Hussin, Faridah, 2017. "Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1108-1118.
  70. Amaral, Luiz Felipe & Souza, Reinaldo Castro & Stevenson, Maxwell, 2008. "A smooth transition periodic autoregressive (STPAR) model for short-term load forecasting," International Journal of Forecasting, Elsevier, vol. 24(4), pages 603-615.
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