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Short-term electricity demand forecasting using double seasonal exponential smoothing

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Cited by:

  1. 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.
  2. Mohan, Neethu & Soman, K.P. & Sachin Kumar, S., 2018. "A data-driven strategy for short-term electric load forecasting using dynamic mode decomposition model," Applied Energy, Elsevier, vol. 232(C), pages 229-244.
  3. James W. Taylor, 2012. "Density Forecasting of Intraday Call Center Arrivals Using Models Based on Exponential Smoothing," Management Science, INFORMS, vol. 58(3), pages 534-549, March.
  4. Smyl, Slawek, 2020. "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting," International Journal of Forecasting, Elsevier, vol. 36(1), pages 75-85.
  5. Lazos, Dimitris & Sproul, Alistair B. & Kay, Merlinde, 2014. "Optimisation of energy management in commercial buildings with weather forecasting inputs: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 587-603.
  6. Dutta, Goutam & Mitra, Krishnendranath, 2015. "Dynamic Pricing of Electricity: A Survey of Related Research," IIMA Working Papers WP2015-08-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
  7. Rob J Hyndman & Shu Fan, 2008. "Density forecasting for long-term peak electricity demand," Monash Econometrics and Business Statistics Working Papers 6/08, Monash University, Department of Econometrics and Business Statistics.
  8. Santiago Gall n & Jorge Barrientos, 2021. "Forecasting the Colombian Electricity Spot Price under a Functional Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 67-74.
  9. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
  10. Trull, Oscar & García-Díaz, J. Carlos & Troncoso, Alicia, 2021. "One-day-ahead electricity demand forecasting in holidays using discrete-interval moving seasonalities," Energy, Elsevier, vol. 231(C).
  11. 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.
  12. Oscar Trull & Juan Carlos García-Díaz & Alicia Troncoso, 2020. "Initialization Methods for Multiple Seasonal Holt–Winters Forecasting Models," Mathematics, MDPI, vol. 8(2), pages 1-16, February.
  13. D J Pedregal & P C Young, 2008. "Development of improved adaptive approaches to electricity demand forecasting," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(8), pages 1066-1076, August.
  14. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
  15. Jaganathan, Srihari & Prakash, P.K.S., 2020. "A combination-based forecasting method for the M4-competition," International Journal of Forecasting, Elsevier, vol. 36(1), pages 98-104.
  16. T. O. Benli, 2016. "A Comparison of Nineteen Various Electricity Consumption Forecasting Approaches and Practicing to Five Different Households in Turkey," Papers 1607.05660, arXiv.org.
  17. Rodrigo Lopez Farias & Vicenç Puig & Hector Rodriguez Rangel & Juan J. Flores, 2018. "Multi-Model Prediction for Demand Forecast in Water Distribution Networks," Energies, MDPI, vol. 11(3), pages 1-21, March.
  18. Janusz Sowinski, 2021. "The Impact of the Selection of Exogenous Variables in the ANFIS Model on the Results of the Daily Load Forecast in the Power Company," Energies, MDPI, vol. 14(2), pages 1-18, January.
  19. 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.
  20. Jackson, Emerson Abraham & Tamuke, Edmund, 2019. "Predicting disaggregated tourist arrivals in Sierra Leone using ARIMA model," MPRA Paper 96845, University Library of Munich, Germany, revised 23 Dec 2019.
  21. Sylvia Mardiana & Ferdinand Saragih & Martani Huseini, 2020. "Forecasting Gasoline Demand in Indonesia Using Time Series," International Journal of Energy Economics and Policy, Econjournals, vol. 10(6), pages 132-145.
  22. Oscar Trull & Angel Peiró-Signes & J. Carlos García-Díaz, 2019. "Electricity Forecasting Improvement in a Destination Using Tourism Indicators," Sustainability, MDPI, vol. 11(13), pages 1-16, July.
  23. Francesco Lisi & Ismail Shah, 2024. "Joint Component Estimation for Electricity Price Forecasting Using Functional Models," Energies, MDPI, vol. 17(14), pages 1-18, July.
  24. Oscar Trull & J. Carlos Garc'ia-D'iaz & Angel Peir'o-Signes, 2024. "mshw, a forecasting library to predict short-term electricity demand based on multiple seasonal Holt-Winters," Papers 2402.10982, arXiv.org.
  25. Tristan Launay & Anne Philippe & Sophie Lamarche, 2015. "Construction of an informative hierarchical prior for a small sample with the help of historical data and application to electricity load forecasting," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(2), pages 361-385, June.
  26. Monika Zielińska-Sitkiewicz & Mariola Chrzanowska & Konrad Furmańczyk & Kacper Paczutkowski, 2021. "Analysis of Electricity Consumption in Poland Using Prediction Models and Neural Networks," Energies, MDPI, vol. 14(20), pages 1-21, October.
  27. Dicembrino, Claudio & Trovato, Giovanni, 2013. "Structural Breaks, Price and Income Elasticity, and Forecast of the Monthly Italian Electricity Demand," MPRA Paper 47653, University Library of Munich, Germany.
  28. Grzegorz Dudek, 2021. "Short-Term Load Forecasting Using Neural Networks with Pattern Similarity-Based Error Weights," Energies, MDPI, vol. 14(11), pages 1-18, May.
  29. 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".
  30. Luca Massidda & Marino Marrocu, 2017. "Decoupling Weather Influence from User Habits for an Optimal Electric Load Forecast System," Energies, MDPI, vol. 10(12), pages 1-16, December.
  31. Liang Qiao & Doudou Liu & Xueliang Yuan & Qingsong Wang & Qiao Ma, 2020. "Generation and Prediction of Construction and Demolition Waste Using Exponential Smoothing Method: A Case Study of Shandong Province, China," Sustainability, MDPI, vol. 12(12), pages 1-12, June.
  32. Ferbar Tratar, Liljana & Mojškerc, Blaž & Toman, Aleš, 2016. "Demand forecasting with four-parameter exponential smoothing," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 162-173.
  33. Giancarlo Aquila & Lucas Barros Scianni Morais & Victor Augusto Durães de Faria & José Wanderley Marangon Lima & Luana Medeiros Marangon Lima & Anderson Rodrigo de Queiroz, 2023. "An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience," Energies, MDPI, vol. 16(21), pages 1-35, November.
  34. Winita Sulandari & Yudho Yudhanto & Paulo Canas Rodrigues, 2022. "The Use of Singular Spectrum Analysis and K-Means Clustering-Based Bootstrap to Improve Multistep Ahead Load Forecasting," Energies, MDPI, vol. 15(16), pages 1-22, August.
  35. AL-Musaylh, Mohanad S. & Deo, Ravinesh C. & Adamowski, Jan F. & Li, Yan, 2019. "Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
  36. EMERSON Abraham Jackson, 2018. "Comparison Between Static And Dynamic Forecast In Autoregressive Integrated Moving Average For Seasonally Adjusted Headline Consumer Price Index," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 70(1), pages 53-65, August.
  37. Xiao, Xun & Mo, Huadong & Zhang, Yinan & Shan, Guangcun, 2022. "Meta-ANN – A dynamic artificial neural network refined by meta-learning for Short-Term Load Forecasting," Energy, Elsevier, vol. 246(C).
  38. Jahanpour, Ehsan & Ko, Hoo Sang & Nof, Shimon Y., 2016. "Collaboration protocols for sustainable wind energy distribution networks," International Journal of Production Economics, Elsevier, vol. 182(C), pages 496-507.
  39. Lifang Xiao & Xiangyang Chen & Hao Wang, 2021. "Calculation and realization of new method grey residual error correction model," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-13, July.
  40. Hill, Arthur V. & Zhang, Weiyong & Burch, Gerald F., 2015. "Forecasting the forecastability quotient for inventory management," International Journal of Forecasting, Elsevier, vol. 31(3), pages 651-663.
  41. 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.
  42. Weide Li & Xuan Yang & Hao Li & Lili Su, 2017. "Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting," Energies, MDPI, vol. 10(1), pages 1-17, January.
  43. Ali K k & Erg n Y kseltan & Mustafa Hekimo lu & Esra Agca Aktunc & Ahmet Y cekaya & Ay e Bilge, 2022. "Forecasting Hourly Electricity Demand Under COVID-19 Restrictions," International Journal of Energy Economics and Policy, Econjournals, vol. 12(1), pages 73-85.
  44. Haben, Stephen & Giasemidis, Georgios & Ziel, Florian & Arora, Siddharth, 2019. "Short term load forecasting and the effect of temperature at the low voltage level," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1469-1484.
  45. Taylor, James W., 2010. "Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles," International Journal of Forecasting, Elsevier, vol. 26(4), pages 627-646, October.
  46. Mladenović Jelena & Lepojević Vinko & Janković-Milić Vesna, 2016. "Modelling and Prognosis of the Export of the Republic of Serbia by Using Seasonal Holt-Winters and Arima Method," Economic Themes, Sciendo, vol. 54(2), pages 233-260, June.
  47. Tanrisever, Fehmi & Derinkuyu, Kursad & Heeren, Michael, 2013. "Forecasting electricity infeed for distribution system networks: An analysis of the Dutch case," Energy, Elsevier, vol. 58(C), pages 247-257.
  48. Magnano, L. & Boland, J.W., 2007. "Generation of synthetic sequences of electricity demand: Application in South Australia," Energy, Elsevier, vol. 32(11), pages 2230-2243.
  49. Pavel V. Matrenin & Vadim Z. Manusov & Alexandra I. Khalyasmaa & Dmitry V. Antonenkov & Stanislav A. Eroshenko & Denis N. Butusov, 2020. "Improving Accuracy and Generalization Performance of Small-Size Recurrent Neural Networks Applied to Short-Term Load Forecasting," Mathematics, MDPI, vol. 8(12), pages 1-17, December.
  50. Arora, Siddharth & Taylor, James W., 2016. "Forecasting electricity smart meter data using conditional kernel density estimation," Omega, Elsevier, vol. 59(PA), pages 47-59.
  51. Aneeque A. Mir & Mohammed Alghassab & Kafait Ullah & Zafar A. Khan & Yuehong Lu & Muhammad Imran, 2020. "A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons," Sustainability, MDPI, vol. 12(15), pages 1-35, July.
  52. 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.
  53. Bedi, Jatin & Toshniwal, Durga, 2019. "Deep learning framework to forecast electricity demand," Applied Energy, Elsevier, vol. 238(C), pages 1312-1326.
  54. Tryggvi Jónsson & Pierre Pinson & Henrik Aa. Nielsen & Henrik Madsen, 2014. "Exponential Smoothing Approaches for Prediction in Real-Time Electricity Markets," Energies, MDPI, vol. 7(6), pages 1-23, June.
  55. Ibrahim, Rouba & Ye, Han & L’Ecuyer, Pierre & Shen, Haipeng, 2016. "Modeling and forecasting call center arrivals: A literature survey and a case study," International Journal of Forecasting, Elsevier, vol. 32(3), pages 865-874.
  56. Bergsteinsson, Hjörleifur G. & Møller, Jan Kloppenborg & Nystrup, Peter & Pálsson, Ólafur Pétur & Guericke, Daniela & Madsen, Henrik, 2021. "Heat load forecasting using adaptive temporal hierarchies," Applied Energy, Elsevier, vol. 292(C).
  57. Lintao Yang & Honggeng Yang, 2019. "Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting," Energies, MDPI, vol. 12(8), pages 1-23, April.
  58. Reham Alhindawi & Yousef Abu Nahleh & Arun Kumar & Nirajan Shiwakoti, 2020. "Projection of Greenhouse Gas Emissions for the Road Transport Sector Based on Multivariate Regression and the Double Exponential Smoothing Model," Sustainability, MDPI, vol. 12(21), pages 1-18, November.
  59. Kamal Chapagain & Somsak Kittipiyakul, 2018. "Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables," Energies, MDPI, vol. 11(4), pages 1-34, April.
  60. Brégère, Margaux & Huard, Malo, 2022. "Online hierarchical forecasting for power consumption data," International Journal of Forecasting, Elsevier, vol. 38(1), pages 339-351.
  61. Aviral Kumar Tiwari & Claudiu T Albulescu & Phouphet Kyophilavong, 2014. "A comparison of different forecasting models of the international trade in India," Economics Bulletin, AccessEcon, vol. 34(1), pages 420-429.
  62. Taylor, James W., 2008. "Exponentially weighted information criteria for selecting among forecasting models," International Journal of Forecasting, Elsevier, vol. 24(3), pages 513-524.
  63. 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.
  64. Corberán-Vallet, Ana & Bermúdez, José D. & Vercher, Enriqueta, 2011. "Forecasting correlated time series with exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 252-265.
  65. Chethana Dharmawardane & Ville Sillanpää & Jan Holmström, 2021. "High-frequency forecasting for grocery point-of-sales: intervention in practice and theoretical implications for operational design," Operations Management Research, Springer, vol. 14(1), pages 38-60, June.
  66. Myoungsoo Kim & Wonik Choi & Youngjun Jeon & Ling Liu, 2019. "A Hybrid Neural Network Model for Power Demand Forecasting," Energies, MDPI, vol. 12(5), pages 1-17, March.
  67. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
  68. 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.
  69. Goutam Dutta & Krishnendranath Mitra, 2017. "A literature review on dynamic pricing of electricity," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1131-1145, October.
  70. Winita Sulandari & Yudho Yudhanto & Sri Subanti & Crisma Devika Setiawan & Riskhia Hapsari & Paulo Canas Rodrigues, 2023. "Comparing the Simple to Complex Automatic Methods with the Ensemble Approach in Forecasting Electrical Time Series Data," Energies, MDPI, vol. 16(22), pages 1-16, November.
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  72. Serrano-Guerrero, Xavier & Briceño-León, Marco & Clairand, Jean-Michel & Escrivá-Escrivá, Guillermo, 2021. "A new interval prediction methodology for short-term electric load forecasting based on pattern recognition," Applied Energy, Elsevier, vol. 297(C).
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  77. 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.
  78. 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.
  79. Jun-Hyeok Kim & Byung-Sung Lee & Chul-Hwan Kim, 2020. "A Study on the Development of Machine-Learning Based Load Transfer Detection Algorithm for Distribution Planning," Energies, MDPI, vol. 13(17), pages 1-12, August.
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  82. Miloš Božić & Miloš Stojanović & Zoran Stajić & Dragan Tasić, 2013. "A New Two-Stage Approach to Short Term Electrical Load Forecasting," Energies, MDPI, vol. 6(4), pages 1-19, April.
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  116. Tasquia Mizan & Sharareh Taghipour, 2021. "A causal model for short‐term time series analysis to predict incoming Medicare workload," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 228-242, March.
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  118. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
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