A Review of Power Prediction Methods Under the COVID-19 Pandemic

Youliang Dong; Changshun Yan

doi:doi:10.11648/j.ijeee.20230805.12

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A Review of Power Prediction Methods Under the COVID-19 Pandemic

Youliang Dong, Changshun Yan

Published in International Journal of Economy, Energy and Environment (Volume 8, Issue 5)

Received: 4 October 2023 Accepted: 6 November 2023 Published: 9 November 2023

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Abstract

Load forecasting, Prediction Models, COVID-19, Time Series Analysis, Combined models, Electricity is the foundation of national construction, and accurate electricity load forecasting is an important guarantee for the normal operation of power systems. During the COVID-19 pandemic, the electricity demand of various countries has fluctuated significantly due to various factors, which has had a certain impact on national development. To assist the government in planning power supply rationally and formulating plans in advance based on electricity demand, it is necessary to accurately predict electricity demand. Therefore, this paper systematically analyzes and introduces the development history of electricity load forecasting technology, which helps to better cope with the impact of the COVID-19 pandemic on the power industry. This paper introduces the research status of electricity load forecasting technology, including time series methods, machine learning methods, deep learning methods, hybrid model methods, and analyzes the advantages and disadvantages of each forecasting method. Establishing a model through these methods can accurately and effectively predict electricity demand, providing technical guarantees and theoretical support for the stable development and long-term construction of the country. Finally, this paper summarizes the current problems in electricity forecasting and the trends of future improvement and development. Through reviewing and summarizing the article, it can provide researchers with ideas and technical routes to solve problems, and also help non-professionals interested in this issue to have a general understanding.

Published in	International Journal of Economy, Energy and Environment (Volume 8, Issue 5)
DOI	10.11648/j.ijeee.20230805.12
Page(s)	113-117
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Load Forecasting, Prediction Models, COVID-19, Time Series Analysis, Combined Models

References

[1]	Jiang, Peng, Y. V. Fan, and Jií Jaromír Kleme. "Impacts of COVID-19 on energy demand and consumption: Challenges, lessons and emerging opportunities." Applied Energy 285 (2021): 116441-.
[2]	Feras A, Khaled N, Lina A, et al. Impact of the COVID-19 Pandemic on Electricity Demand and Load Forecasting [J]. Sustainability, 2021, 13 (3).
[3]	Sarah Hadri; Mehdi Najib; Mohamed Bakhouya; Youssef Fakhri; Mohamed El Arroussi; "Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings", ENERGIES, 2021.
[4]	Angelaccio M. Forecasting Public Electricity Consumption with ARIMA Model: A Case Study from Italian Municipalities Energy Data [C] // 2019 International Symposium on Advanced Electrical and Communication Technologies (ISAECT). 2019. DOI: 10.1109/ISAECT47714.2019.9069696.
[5]	Ismail Z H, Mahpol K A. SARIMA Model for Forecasting Malaysian Electricity Generated [J]. Matematika, 2005. DOI: 10.11113/MATEMATIKA.V21.N.522.
[6]	Ohtsuka Y, Kakamu K. Space-Time Model versus VAR Model: Forecasting Electricity demand in Japan [J]. Journal of Forecasting, 2013, 32 (1): 75-85. DOI: 10.1002/for.1255.
[7]	Junhui Huang; M. Algahtani; S. Kaewunruen; "Energy Forecasting in A Public Building: A Benchmarking Analysis on Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) Networks", APPLIED SCIENCES, 2022.
[8]	Gulati Payal, Kumar Anil, and Bhardwaj Raghav."Impact of Covid19 on electricity load in Haryana (India)." International journal of energy research 45. 2 (2020). doi: 10.1002/ER.6008.
[9]	Abulibdeh Ammar, Zaidan Esmat, and Jabbar Rateb. "The impact of COVID-19 pandemic on electricity consumption and electricity demand forecasting accuracy: Empirical evidence from the state of Qatar." Energy Strategy Reviews 44. (2022). doi: 10.1016/J.ESR.2022.100980.
[10]	Ku, Arthur Lin, et al. "Changes in hourly electricity consumption under COVID mandates: A glance to future hourly residential power consumption pattern with remote work in Arizona." Applied Energy 310 (2022): 118539.
[11]	Fumo N, Biswas R M. Regression analysis for prediction of residential energy consumption [J]. Renewable and Sustainable Energy Reviews, 2015, 47.
[12]	C. Hora; F. Dan; G. Bendea; C. Secui; "Residential Short-Term Load Forecasting During Atypical Consumption Behavior", ENERGIES, 2022.
[13]	Research from Federal University Pernambuco Yields New Study Findings on Machine Learning (Short- and Very Short-Term Firm-Level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models) [J]. Robotics & Machine Learning Daily News, 2022 (Mar. 1).
[14]	Andrei M. Tudose; Irina I. Picioroaga; Dorian O. Sidea; Constantin Bulac; Valentin A. Boicea; "Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study", ENERGIES, 2021. (IF: 3).
[15]	Badhon Saha; Kazi Firoz Ahmed; Shoumitra Saha; Md. Thoufiqul Islam; "Short-Term Electrical Load Forecasting Via Deep Learning Algorithms to Mitigate The Impact of COVID-19 Pandemic on Power Demand", 2021 INTERNATIONAL CONFERENCE ON AUTOMATION, CONTROL AND..., 2021.
[16]	Zhang Y, Kong W, Dong Z Y, et al. Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network [J]. IEEE Transactions on Smart Grid, 2019.
[17]	Jiefeng Liu; Zhenhao Zhang; Xianhao Fan; Yiyi Zhang; Jiaqi Wang; Ke Zhou; Shuo Liang; Xiaoyong Yu; Wei Zhang; "Power System Load Forecasting Using Mobility Optimization and Multi-task Learning in COVID-19", APPLIED ENERGY, 2022.
[18]	Li C, Chen Z, Liu J, et al. Power Load Forecasting Based on the Combined Model of LSTM and XGBoost [C] // the 2019 the International Conference. 2019. DOI: 10.1145/3357777.3357792.
[19]	Fan M, Hu Y, Zhang X, et al. Short-term Load Forecasting for Distribution Network Using Decomposition with Ensemble prediction [C] // 2019 Chinese Automation Congress (CAC). IEEE, 2019. DOI: 10.1109/CAC48633.2019.8997169.
[20]	M. Alhussein, K. Aurangzeb and S. I. Haider, "Hybrid CNN-LSTM Model for Short-Term Individual Household Load Forecasting," in IEEE Access, vol. 8, pp. 180544-180557, 2020, doi: 10.1109/ACCESS.2020.3028281.
[21]	Wang, Qiang, S. Li, and F. Jiang. "Uncovering the impact of the COVID-19 pandemic on energy consumption: New insight from difference between pandemic-free scenario and actual electricity consumption in China." Journal of Cleaner Production 6 (2021): 127897.

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APA Style

Dong, Y., Yan, C. (2023). A Review of Power Prediction Methods Under the COVID-19 Pandemic. International Journal of Economy, Energy and Environment, 8(5), 113-117. https://doi.org/10.11648/j.ijeee.20230805.12

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ACS Style

Dong, Y.; Yan, C. A Review of Power Prediction Methods Under the COVID-19 Pandemic. Int. J. Econ. Energy Environ. 2023, 8(5), 113-117. doi: 10.11648/j.ijeee.20230805.12

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AMA Style

Dong Y, Yan C. A Review of Power Prediction Methods Under the COVID-19 Pandemic. Int J Econ Energy Environ. 2023;8(5):113-117. doi: 10.11648/j.ijeee.20230805.12

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@article{10.11648/j.ijeee.20230805.12,
  author = {Youliang Dong and Changshun Yan},
  title = {A Review of Power Prediction Methods Under the COVID-19 Pandemic},
  journal = {International Journal of Economy, Energy and Environment},
  volume = {8},
  number = {5},
  pages = {113-117},
  doi = {10.11648/j.ijeee.20230805.12},
  url = {https://doi.org/10.11648/j.ijeee.20230805.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijeee.20230805.12},
  abstract = {Load forecasting, Prediction Models, COVID-19, Time Series Analysis, Combined models, Electricity is the foundation of national construction, and accurate electricity load forecasting is an important guarantee for the normal operation of power systems. During the COVID-19 pandemic, the electricity demand of various countries has fluctuated significantly due to various factors, which has had a certain impact on national development. To assist the government in planning power supply rationally and formulating plans in advance based on electricity demand, it is necessary to accurately predict electricity demand. Therefore, this paper systematically analyzes and introduces the development history of electricity load forecasting technology, which helps to better cope with the impact of the COVID-19 pandemic on the power industry. This paper introduces the research status of electricity load forecasting technology, including time series methods, machine learning methods, deep learning methods, hybrid model methods, and analyzes the advantages and disadvantages of each forecasting method. Establishing a model through these methods can accurately and effectively predict electricity demand, providing technical guarantees and theoretical support for the stable development and long-term construction of the country. Finally, this paper summarizes the current problems in electricity forecasting and the trends of future improvement and development. Through reviewing and summarizing the article, it can provide researchers with ideas and technical routes to solve problems, and also help non-professionals interested in this issue to have a general understanding.
},
 year = {2023}
}

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TY  - JOUR
T1  - A Review of Power Prediction Methods Under the COVID-19 Pandemic
AU  - Youliang Dong
AU  - Changshun Yan
Y1  - 2023/11/09
PY  - 2023
N1  - https://doi.org/10.11648/j.ijeee.20230805.12
DO  - 10.11648/j.ijeee.20230805.12
T2  - International Journal of Economy, Energy and Environment
JF  - International Journal of Economy, Energy and Environment
JO  - International Journal of Economy, Energy and Environment
SP  - 113
EP  - 117
PB  - Science Publishing Group
SN  - 2575-5021
UR  - https://doi.org/10.11648/j.ijeee.20230805.12
AB  - Load forecasting, Prediction Models, COVID-19, Time Series Analysis, Combined models, Electricity is the foundation of national construction, and accurate electricity load forecasting is an important guarantee for the normal operation of power systems. During the COVID-19 pandemic, the electricity demand of various countries has fluctuated significantly due to various factors, which has had a certain impact on national development. To assist the government in planning power supply rationally and formulating plans in advance based on electricity demand, it is necessary to accurately predict electricity demand. Therefore, this paper systematically analyzes and introduces the development history of electricity load forecasting technology, which helps to better cope with the impact of the COVID-19 pandemic on the power industry. This paper introduces the research status of electricity load forecasting technology, including time series methods, machine learning methods, deep learning methods, hybrid model methods, and analyzes the advantages and disadvantages of each forecasting method. Establishing a model through these methods can accurately and effectively predict electricity demand, providing technical guarantees and theoretical support for the stable development and long-term construction of the country. Finally, this paper summarizes the current problems in electricity forecasting and the trends of future improvement and development. Through reviewing and summarizing the article, it can provide researchers with ideas and technical routes to solve problems, and also help non-professionals interested in this issue to have a general understanding.

VL  - 8
IS  - 5
ER  -

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Author Information

Youliang Dong

Faculty of Information Technology, Beijing University of Technology, Beijing, China

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Changshun Yan

Faculty of Information Technology, Beijing University of Technology, Beijing, China

Contact Email

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Plain Text BibTeX RIS

APA Style

Dong, Y., Yan, C. (2023). A Review of Power Prediction Methods Under the COVID-19 Pandemic. International Journal of Economy, Energy and Environment, 8(5), 113-117. https://doi.org/10.11648/j.ijeee.20230805.12

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ACS Style

Dong, Y.; Yan, C. A Review of Power Prediction Methods Under the COVID-19 Pandemic. Int. J. Econ. Energy Environ. 2023, 8(5), 113-117. doi: 10.11648/j.ijeee.20230805.12

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AMA Style

Dong Y, Yan C. A Review of Power Prediction Methods Under the COVID-19 Pandemic. Int J Econ Energy Environ. 2023;8(5):113-117. doi: 10.11648/j.ijeee.20230805.12

Copy | Download

@article{10.11648/j.ijeee.20230805.12,
  author = {Youliang Dong and Changshun Yan},
  title = {A Review of Power Prediction Methods Under the COVID-19 Pandemic},
  journal = {International Journal of Economy, Energy and Environment},
  volume = {8},
  number = {5},
  pages = {113-117},
  doi = {10.11648/j.ijeee.20230805.12},
  url = {https://doi.org/10.11648/j.ijeee.20230805.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijeee.20230805.12},
  abstract = {Load forecasting, Prediction Models, COVID-19, Time Series Analysis, Combined models, Electricity is the foundation of national construction, and accurate electricity load forecasting is an important guarantee for the normal operation of power systems. During the COVID-19 pandemic, the electricity demand of various countries has fluctuated significantly due to various factors, which has had a certain impact on national development. To assist the government in planning power supply rationally and formulating plans in advance based on electricity demand, it is necessary to accurately predict electricity demand. Therefore, this paper systematically analyzes and introduces the development history of electricity load forecasting technology, which helps to better cope with the impact of the COVID-19 pandemic on the power industry. This paper introduces the research status of electricity load forecasting technology, including time series methods, machine learning methods, deep learning methods, hybrid model methods, and analyzes the advantages and disadvantages of each forecasting method. Establishing a model through these methods can accurately and effectively predict electricity demand, providing technical guarantees and theoretical support for the stable development and long-term construction of the country. Finally, this paper summarizes the current problems in electricity forecasting and the trends of future improvement and development. Through reviewing and summarizing the article, it can provide researchers with ideas and technical routes to solve problems, and also help non-professionals interested in this issue to have a general understanding.
},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - A Review of Power Prediction Methods Under the COVID-19 Pandemic
AU  - Youliang Dong
AU  - Changshun Yan
Y1  - 2023/11/09
PY  - 2023
N1  - https://doi.org/10.11648/j.ijeee.20230805.12
DO  - 10.11648/j.ijeee.20230805.12
T2  - International Journal of Economy, Energy and Environment
JF  - International Journal of Economy, Energy and Environment
JO  - International Journal of Economy, Energy and Environment
SP  - 113
EP  - 117
PB  - Science Publishing Group
SN  - 2575-5021
UR  - https://doi.org/10.11648/j.ijeee.20230805.12
AB  - Load forecasting, Prediction Models, COVID-19, Time Series Analysis, Combined models, Electricity is the foundation of national construction, and accurate electricity load forecasting is an important guarantee for the normal operation of power systems. During the COVID-19 pandemic, the electricity demand of various countries has fluctuated significantly due to various factors, which has had a certain impact on national development. To assist the government in planning power supply rationally and formulating plans in advance based on electricity demand, it is necessary to accurately predict electricity demand. Therefore, this paper systematically analyzes and introduces the development history of electricity load forecasting technology, which helps to better cope with the impact of the COVID-19 pandemic on the power industry. This paper introduces the research status of electricity load forecasting technology, including time series methods, machine learning methods, deep learning methods, hybrid model methods, and analyzes the advantages and disadvantages of each forecasting method. Establishing a model through these methods can accurately and effectively predict electricity demand, providing technical guarantees and theoretical support for the stable development and long-term construction of the country. Finally, this paper summarizes the current problems in electricity forecasting and the trends of future improvement and development. Through reviewing and summarizing the article, it can provide researchers with ideas and technical routes to solve problems, and also help non-professionals interested in this issue to have a general understanding.

VL  - 8
IS  - 5
ER  -

Copy | Download