Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19
Author
Abstract
Suggested Citation
DOI: 10.1016/j.apenergy.2020.116339
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Zhao, Huiru & Guo, Sen, 2016. "An optimized grey model for annual power load forecasting," Energy, Elsevier, vol. 107(C), pages 272-286.
- Crompton, Paul & Wu, Yanrui, 2005.
"Energy consumption in China: past trends and future directions,"
Energy Economics, Elsevier, vol. 27(1), pages 195-208, January.
- Paul Crompton & Yanrui Wu, 2004. "Energy Consumption in China: Past Trends and Future Directions," Economics Discussion / Working Papers 04-22, The University of Western Australia, Department of Economics.
- Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
- Wang, Qiang & Li, Shuyu & Li, Rongrong & Ma, Minglu, 2018. "Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model," Energy, Elsevier, vol. 160(C), pages 378-387.
- Long, Yin & Yoshida, Yoshikuni & Meng, Jing & Guan, Dabo & Yao, Liming & Zhang, Haoran, 2019. "Unequal age-based household emission and its monthly variation embodied in energy consumption – A cases study of Tokyo, Japan," Applied Energy, Elsevier, vol. 247(C), pages 350-362.
- Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
- Ferguson, Ross & Wilkinson, William & Hill, Robert, 2000. "Electricity use and economic development," Energy Policy, Elsevier, vol. 28(13), pages 923-934, November.
- Mohamed, Zaid & Bodger, Pat, 2005. "Forecasting electricity consumption in New Zealand using economic and demographic variables," Energy, Elsevier, vol. 30(10), pages 1833-1843.
- Luo, Xing & Wang, Jihong & Dooner, Mark & Clarke, Jonathan, 2015. "Overview of current development in electrical energy storage technologies and the application potential in power system operation," Applied Energy, Elsevier, vol. 137(C), pages 511-536.
- Al-Bajjali, Saif Kayed & Shamayleh, Adel Yacoub, 2018. "Estimating the determinants of electricity consumption in Jordan," Energy, Elsevier, vol. 147(C), pages 1311-1320.
- Zheng, Jianqin & Zhang, Haoran & Dai, Yuanhao & Wang, Bohong & Zheng, Taicheng & Liao, Qi & Liang, Yongtu & Zhang, Fengwei & Song, Xuan, 2020. "Time series prediction for output of multi-region solar power plants," Applied Energy, Elsevier, vol. 257(C).
- Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
- Ding, Song & Hipel, Keith W. & Dang, Yao-guo, 2018. "Forecasting China's electricity consumption using a new grey prediction model," Energy, Elsevier, vol. 149(C), pages 314-328.
- Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
- Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques," Energy, Elsevier, vol. 161(C), pages 821-831.
- Xu, Ning & Dang, Yaoguo & Gong, Yande, 2017. "Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China," Energy, Elsevier, vol. 118(C), pages 473-480.
- Akihiro Otsuka, 2019. "Natural disasters and electricity consumption behavior: a case study of the 2011 Great East Japan Earthquake," Asia-Pacific Journal of Regional Science, Springer, vol. 3(3), pages 887-910, October.
- Zhang, Haoran & Song, Xuan & Xia, Tianqi & Yuan, Meng & Fan, Zipei & Shibasaki, Ryosuke & Liang, Yongtu, 2018. "Battery electric vehicles in Japan: Human mobile behavior based adoption potential analysis and policy target response," Applied Energy, Elsevier, vol. 220(C), pages 527-535.
- Xiong, Ping-ping & Dang, Yao-guo & Yao, Tian-xiang & Wang, Zheng-xin, 2014. "Optimal modeling and forecasting of the energy consumption and production in China," Energy, Elsevier, vol. 77(C), pages 623-634.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
- Neilson Luniere Vilaça & Marly Guimarães Fernandes Costa & Cicero Ferreira Fernandes Costa Filho, 2023. "A Hybrid Deep Neural Network Architecture for Day-Ahead Electricity Forecasting: Post-COVID Paradigm," Energies, MDPI, vol. 16(8), pages 1-14, April.
- Hu, Chenxi & Zhang, Jun & Yuan, Hongxia & Gao, Tianlu & Jiang, Huaiguang & Yan, Jing & Wenzhong Gao, David & Wang, Fei-Yue, 2022. "Black swan event small-sample transfer learning (BEST-L) and its case study on electrical power prediction in COVID-19," Applied Energy, Elsevier, vol. 309(C).
- Indre Siksnelyte-Butkiene, 2021. "Impact of the COVID-19 Pandemic to the Sustainability of the Energy Sector," Sustainability, MDPI, vol. 13(23), pages 1-19, November.
- Zhang, Yunxin & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2023. "A novel grey Lotka–Volterra model driven by the mechanism of competition and cooperation for energy consumption forecasting," Energy, Elsevier, vol. 264(C).
- Liu, Zhihong & ul Islam, Misbah & Alarifi, Ghadah Abdulrahman & Cong, Phan The & Khudoykulov, Khurshid & Quynh, Le Nhu & Hossain, Md. Shamim, 2023. "Does energy efficiency mediate a green economic recovery? Evidence from China," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 802-815.
- Muhammad Shahid Mastoi & Hafiz Mudassir Munir & Shenxian Zhuang & Mannan Hassan & Muhammad Usman & Ahmad Alahmadi & Basem Alamri, 2022. "A Critical Analysis of the Impact of Pandemic on China’s Electricity Usage Patterns and the Global Development of Renewable Energy," IJERPH, MDPI, vol. 19(8), pages 1-30, April.
- Tomasz Cieślik & Piotr Narloch & Adam Szurlej & Krzysztof Kogut, 2022. "Indirect Impact of the COVID-19 Pandemic on Natural Gas Consumption by Commercial Consumers in a Selected City in Poland," Energies, MDPI, vol. 15(4), pages 1-18, February.
- Li, Jinghua & Luo, Yichen & Wei, Shanyang, 2022. "Long-term electricity consumption forecasting method based on system dynamics under the carbon-neutral target," Energy, Elsevier, vol. 244(PA).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Ding, Song & Hipel, Keith W. & Dang, Yao-guo, 2018. "Forecasting China's electricity consumption using a new grey prediction model," Energy, Elsevier, vol. 149(C), pages 314-328.
- 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.
- 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.
- Zhu, Xiaoyue & Dang, Yaoguo & Ding, Song, 2020. "Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China," Energy, Elsevier, vol. 190(C).
- 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.
- Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
- Wang, Lin & Hu, Huanling & Ai, Xue-Yi & Liu, Hua, 2018. "Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm," Energy, Elsevier, vol. 153(C), pages 801-815.
- Atif Maqbool Khan & Magdalena Osińska, 2021. "How to Predict Energy Consumption in BRICS Countries?," Energies, MDPI, vol. 14(10), pages 1-21, May.
- Akdi, Yılmaz & Gölveren, Elif & Okkaoğlu, Yasin, 2020. "Daily electrical energy consumption: Periodicity, harmonic regression method and forecasting," Energy, Elsevier, vol. 191(C).
- Zhang, Yunxin & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2023. "A novel grey Lotka–Volterra model driven by the mechanism of competition and cooperation for energy consumption forecasting," Energy, Elsevier, vol. 264(C).
- 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.
- Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
- Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
- 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.
- 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).
- Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
- Ding, Song, 2018. "A novel self-adapting intelligent grey model for forecasting China's natural-gas demand," Energy, Elsevier, vol. 162(C), pages 393-407.
- Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques," Energy, Elsevier, vol. 161(C), pages 821-831.
- 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.
- Du, Pei & Guo, Ju'e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2022. "A novel two-stage seasonal grey model for residential electricity consumption forecasting," Energy, Elsevier, vol. 258(C).
More about this item
Keywords
Electricity consumption gap; Prediction-based analysis; IMSGM(1; 1); COVID-19;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920317219. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.