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Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China

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  1. 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, Elsevier, vol. 65(C), pages 452-461.
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  6. Nibedita Mahanta & Ruma Talukdar, 2024. "Forecasting of Electricity Consumption by Seasonal Autoregressive Integrated Moving Average Model in Assam, India," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, vol. 14(5), pages 393-400, September.
  7. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
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  16. Wang, Zheng-Xin & Wang, Zhi-Wei & Li, Qin, 2020. "Forecasting the industrial solar energy consumption using a novel seasonal GM(1,1) model with dynamic seasonal adjustment factors," Energy, Elsevier, vol. 200(C).
  17. Hafeez, Ghulam & Khan, Imran & Jan, Sadaqat & Shah, Ibrar Ali & Khan, Farrukh Aslam & Derhab, Abdelouahid, 2021. "A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid," Applied Energy, Elsevier, vol. 299(C).
  18. Zhang, Xinyi, 2026. "How does digital marketing enhance the accuracy of performance forecasts?," Finance Research Letters, Elsevier, vol. 96(C).
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  20. Anca Mehedintu & Mihaela Sterpu & Georgeta Soava, 2018. "Estimation and Forecasts for the Share of Renewable Energy Consumption in Final Energy Consumption by 2020 in the European Union," Sustainability, MDPI, vol. 10(5), pages 1-22, May.
  21. Moustris, K. & Kavadias, K.A. & Zafirakis, D. & Kaldellis, J.K., 2020. "Medium, short and very short-term prognosis of load demand for the Greek Island of Tilos using artificial neural networks and human thermal comfort-discomfort biometeorological data," Renewable Energy, Elsevier, vol. 147(P1), pages 100-109.
  22. Liu, Tianhong & Qi, Shengli & Qiao, Xianzhu & Liu, Sixing, 2024. "A hybrid short-term wind power point-interval prediction model based on combination of improved preprocessing methods and entropy weighted GRU quantile regression network," Energy, Elsevier, vol. 288(C).
  23. Yuewei Liu & Shenghui Zhang & Xuejun Chen & Jianzhou Wang, 2018. "Artificial Combined Model Based on Hybrid Nonlinear Neural Network Models and Statistics Linear Models—Research and Application for Wind Speed Forecasting," Sustainability, MDPI, vol. 10(12), pages 1-30, December.
  24. Miguel A. Jaramillo-Morán & Daniel Fernández-Martínez & Agustín García-García & Diego Carmona-Fernández, 2021. "Improving Artificial Intelligence Forecasting Models Performance with Data Preprocessing: European Union Allowance Prices Case Study," Energies, MDPI, vol. 14(23), pages 1-23, November.
  25. Yang, Qing & Zhang, Lei & Zou, Shaohui & Zhang, Jinsuo, 2020. "Intertemporal optimization of the coal production capacity in China in terms of uncertain demand, economy, environment, and energy security," Energy Policy, Elsevier, vol. 139(C).
  26. Zeng, Bo & Li, Chuan, 2016. "Forecasting the natural gas demand in China using a self-adapting intelligent grey model," Energy, Elsevier, vol. 112(C), pages 810-825.
  27. Prado, Francisco & Minutolo, Marcel C. & Kristjanpoller, Werner, 2020. "Forecasting based on an ensemble Autoregressive Moving Average - Adaptive neuro - Fuzzy inference system – Neural network - Genetic Algorithm Framework," Energy, Elsevier, vol. 197(C).
  28. Yechi Zhang & Jianzhou Wang & Haiyan Lu, 2019. "Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting," Energies, MDPI, vol. 12(10), pages 1-27, May.
  29. Jianguo Zhou & Xiaolei Xu & Xuejing Huo & Yushuo Li, 2019. "Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks," Sustainability, MDPI, vol. 11(3), pages 1-23, January.
  30. Manuel Jaramillo & Diego Carrión, 2022. "An Adaptive Strategy for Medium-Term Electricity Consumption Forecasting for Highly Unpredictable Scenarios: Case Study Quito, Ecuador during the Two First Years of COVID-19," Energies, MDPI, vol. 15(22), pages 1-19, November.
  31. Yucekaya, Ahmet & Bilge, Ayse H. & Yukseltan, Ergun & Agca Aktunc, Esra, 2024. "Segregation of Hourly Electricity Consumption: Quantification of Demand Types Using Fourier Transform," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, vol. 15(1), pages 384-396, December.
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  33. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra, 2017. "Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation," Applied Energy, Elsevier, vol. 193(C), pages 287-296.
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  36. Yongju Son & Yeunggurl Yoon & Jintae Cho & Sungyun Choi, 2022. "Cloud Cover Forecast Based on Correlation Analysis on Satellite Images for Short-Term Photovoltaic Power Forecasting," Sustainability, MDPI, vol. 14(8), pages 1-24, April.
  37. Jianzhou Wang & Ling Xiao & Jun Shi, 2014. "The Combination Forecasting of Electricity Price Based on Price Spikes Processing: A Case Study in South Australia," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
  38. Zhou, Qingguo & Wang, Chen & Zhang, Gaofeng, 2019. "Hybrid forecasting system based on an optimal model selection strategy for different wind speed forecasting problems," Applied Energy, Elsevier, vol. 250(C), pages 1559-1580.
  39. Murat Yalçıntaş & Melih Bulu & Murat Küçükvar & Hamidreza Samadi, 2015. "A Framework for Sustainable Urban Water Management through Demand and Supply Forecasting: The Case of Istanbul," Sustainability, MDPI, vol. 7(8), pages 1-18, August.
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