Machine learning model to predict electricity demand and thermal generation during the pandemic
2021 China Automation Congress, CAC 2021
; : 4690-4695, 2021.
Article
in English
| Scopus | ID: covidwho-1806893
ABSTRACT
Owing to the global lockdown caused by the pandemic of COVID-19, the electricity demand is greatly affected, and the electricity market is also constantly fluctuating. During the pandemic period, the prediction of electricity demand is crucial to the economy and power dispatching. In this study, we combine the pandemic data and government anti-pandemic policies data to predict the electricity demand of the Contiguous United States by using the artificial neural network and recurrent neural network. In addition, the linear regression method is used to forecast the thermal generation with total generation data. Some experiments have developed to verify the effectiveness of the model. Then the model is used to forecast electricity demand and thermal generation under different policies and pandemic development, and the result were analyzed. © 2021 IEEE
Electricity Demand Prediction; Machine Learning; Thermal Generation; Electric load dispatching; Forecasting; Recurrent neural networks; Regression analysis; Demand generation; Demand prediction; Electricity demands; Linear regression methods; Machine learning models; Machine-learning; Power dispatching; Electric power utilization
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
2021 China Automation Congress, CAC 2021
Year:
2021
Document Type:
Article
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