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Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques

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  1. Jun Hao & Xiaolei Sun & Qianqian Feng, 2020. "A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm," Energies, MDPI, vol. 13(3), pages 1-25, January.
  2. Shuyu Li & Rongrong Li, 2019. "Evaluating Energy Sustainability Using the Pressure-State-Response and Improved Matter-Element Extension Models: Case Study of China," Sustainability, MDPI, vol. 11(1), pages 1-20, January.
  3. Sun-Youn Shin & Han-Gyun Woo, 2022. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms," Energies, MDPI, vol. 15(13), pages 1-20, July.
  4. Muhammad Zubair & Ahmed Bilal Awan, 2021. "Economic viability of solar energy export from the Middle East and North Africa to Europe and South Asia," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(12), pages 17986-18007, December.
  5. Lili Wang & Lina Zhan & Rongrong Li, 2019. "Prediction of the Energy Demand Trend in Middle Africa—A Comparison of MGM, MECM, ARIMA and BP Models," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
  6. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2019. "Will Trump's coal revival plan work? - Comparison of results based on the optimal combined forecasting technique and an extended IPAT forecasting technique," Energy, Elsevier, vol. 169(C), pages 762-775.
  7. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
  8. Abraham Yeboah & Ofosu Agyekum & Vida Owusu-Prempeh & Kwadwo Boateng Prempeh, 2023. "Using social presence theory to predict online consumer engagement in the emerging markets," Future Business Journal, Springer, vol. 9(1), pages 1-18, December.
  9. Faheem Jan & Ismail Shah & Sajid Ali, 2022. "Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis," Energies, MDPI, vol. 15(9), pages 1-15, May.
  10. Carlo Andrea Bollino & Maria Chiara D’Errico, 2022. "Electricity Demand Elasticity, Mobility, and COVID-19 Contagion Nexus in the Italian Day-Ahead Electricity Market," Energies, MDPI, vol. 15(20), pages 1-26, October.
  11. Stéfano Frizzo Stefenon & Roberto Zanetti Freire & Leandro dos Santos Coelho & Luiz Henrique Meyer & Rafael Bartnik Grebogi & William Gouvêa Buratto & Ademir Nied, 2020. "Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System," Energies, MDPI, vol. 13(2), pages 1-19, January.
  12. Shuyu Li & Xuan Yang & Rongrong Li, 2019. "Forecasting Coal Consumption in India by 2030: Using Linear Modified Linear (MGM-ARIMA) and Linear Modified Nonlinear (BP-ARIMA) Combined Models," Sustainability, MDPI, vol. 11(3), pages 1-19, January.
  13. Wang, Qiang & Song, Xiaoxing & Li, Rongrong, 2018. "A novel hybridization of nonlinear grey model and linear ARIMA residual correction for forecasting U.S. shale oil production," Energy, Elsevier, vol. 165(PB), pages 1320-1331.
  14. Lu, Hongfang & Ma, Xin & Azimi, Mohammadamin, 2020. "US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model," Energy, Elsevier, vol. 194(C).
  15. Rao, Congjun & Zhang, Yue & Wen, Jianghui & Xiao, Xinping & Goh, Mark, 2023. "Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model," Energy, Elsevier, vol. 263(PC).
  16. Xue-Ting Jiang & Min Su & Rongrong Li, 2018. "Decomposition Analysis in Electricity Sector Output from Carbon Emissions in China," Sustainability, MDPI, vol. 10(9), pages 1-18, September.
  17. Yousaf Raza, Muhammad & Lin, Boqiang, 2021. "Oil for Pakistan: What are the main factors affecting the oil import?," Energy, Elsevier, vol. 237(C).
  18. Chaturvedi, Shobhit & Rajasekar, Elangovan & Natarajan, Sukumar & McCullen, Nick, 2022. "A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India," Energy Policy, Elsevier, vol. 168(C).
  19. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
  20. Xinyu Han & Rongrong Li, 2019. "Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model," Energies, MDPI, vol. 12(17), pages 1-24, August.
  21. Peng, Cheng & Chen, Heng & Lin, Chaoran & Guo, Shuang & Yang, Zhi & Chen, Ke, 2021. "A framework for evaluating energy security in China: Empirical analysis of forecasting and assessment based on energy consumption," Energy, Elsevier, vol. 234(C).
  22. Hyunsoo Kim & Jiseok Jeong & Changwan Kim, 2022. "Daily Peak-Electricity-Demand Forecasting Based on Residual Long Short-Term Network," Mathematics, MDPI, vol. 10(23), pages 1-17, November.
  23. Aarti Mehta Sharma & Saina Baby & Varsha Raghu, 2024. "Forecasting High Speed Diesel Demand in India with Econometric and Machine Learning Methods," International Journal of Energy Economics and Policy, Econjournals, vol. 14(1), pages 496-506, January.
  24. 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.
  25. Ramya Kuppusamy & Srete Nikolovski & Yuvaraja Teekaraman, 2023. "Review of Machine Learning Techniques for Power Quality Performance Evaluation in Grid-Connected Systems," Sustainability, MDPI, vol. 15(20), pages 1-29, October.
  26. Geng Wu & Yi-Chung Hu & Yu-Jing Chiu & Shu-Ju Tsao, 2023. "A new multivariate grey prediction model for forecasting China’s regional energy consumption," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(5), pages 4173-4193, May.
  27. Sinha, Rakesh Kumar & Chaturvedi, Nitin Dutt, 2019. "A review on carbon emission reduction in industries and planning emission limits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
  28. Wang, Qiang & Song, Xiaoxin, 2019. "Forecasting China's oil consumption: A comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM," Energy, Elsevier, vol. 183(C), pages 160-171.
  29. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "China's dependency on foreign oil will exceed 80% by 2030: Developing a novel NMGM-ARIMA to forecast China's foreign oil dependence from two dimensions," Energy, Elsevier, vol. 163(C), pages 151-167.
  30. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
  31. Su, Huai & Zio, Enrico & Zhang, Jinjun & Xu, Mingjing & Li, Xueyi & Zhang, Zongjie, 2019. "A hybrid hourly natural gas demand forecasting method based on the integration of wavelet transform and enhanced Deep-RNN model," Energy, Elsevier, vol. 178(C), pages 585-597.
  32. Minglu Ma & Zhuangzhuang Wang, 2019. "Prediction of the Energy Consumption Variation Trend in South Africa based on ARIMA, NGM and NGM-ARIMA Models," Energies, MDPI, vol. 13(1), pages 1-15, December.
  33. 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).
  34. Wenting Zhao & Juanjuan Zhao & Xilong Yao & Zhixin Jin & Pan Wang, 2019. "A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand," Energies, MDPI, vol. 12(7), pages 1-28, April.
  35. Marwa Salah EIDin Fahmy & Farhan Ahmed & Farah Durani & Štefan Bojnec & Mona Mohamed Ghareeb, 2023. "Predicting Electricity Consumption in the Kingdom of Saudi Arabia," Energies, MDPI, vol. 16(1), pages 1-20, January.
  36. Zhixiong Weng & Yuqi Song & Hao Ma & Zhong Ma & Tingting Liu, 2023. "Forecasting energy demand, structure, and CO2 emission: a case study of Beijing, China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 10369-10391, September.
  37. Huang, Liqiao & Liao, Qi & Qiu, Rui & Liang, Yongtu & Long, Yin, 2021. "Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19," Applied Energy, Elsevier, vol. 283(C).
  38. Xu, Haitao & Pan, Xiongfeng & Guo, Shucen & Lu, Yuduo, 2021. "Forecasting Chinese CO2 emission using a non-linear multi-agent intertemporal optimization model and scenario analysis," Energy, Elsevier, vol. 228(C).
  39. Wang, Qiang & Li, Shuyu & Zhang, Min & Li, Rongrong, 2022. "Impact of COVID-19 pandemic on oil consumption in the United States: A new estimation approach," Energy, Elsevier, vol. 239(PC).
  40. Zhineng Hu & Jing Ma & Liangwei Yang & Liming Yao & Meng Pang, 2019. "Monthly electricity demand forecasting using empirical mode decomposition-based state space model," Energy & Environment, , vol. 30(7), pages 1236-1254, November.
  41. Yeqi An & Yulin Zhou & Rongrong Li, 2019. "Forecasting India’s Electricity Demand Using a Range of Probabilistic Methods," Energies, MDPI, vol. 12(13), pages 1-24, July.
  42. Rafati, Amir & Joorabian, Mahmood & Mashhour, Elaheh, 2020. "An efficient hour-ahead electrical load forecasting method based on innovative features," Energy, Elsevier, vol. 201(C).
  43. Liu, Jixiang & Tian, Shu & Wang, Qingsong & Xu, Yue & Zhang, Yujie & Yuan, Xueliang & Ma, Qiao & Ma, Haichao & Liu, Chengqing, 2023. "The regulation path of coal consumption based on the total reduction index—a case study in Shandong Province, China," Energy, Elsevier, vol. 262(PB).
  44. Shian-Chang Huang & Cheng-Feng Wu, 2018. "Energy Commodity Price Forecasting with Deep Multiple Kernel Learning," Energies, MDPI, vol. 11(11), pages 1-16, November.
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