Short-term load forecasting based on machine learning and epidemic association features
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control
; 50(23):2023/08/01 00:00:00.000, 2022.
Article
in Chinese
| Scopus | ID: covidwho-2228860
ABSTRACT
Accurate power load forecasting is an important guarantee for normal operation of a power system. There have been problems of large fluctuations in load demand and difficulty in modeling historical reference load during the COVID-19 outbreak. Thus this paper proposes a short-term load forecasting method based on machine learning, silent index and rolling anxiety index. First, Google mobility data and epidemic data are used to construct the silent index and rolling anxiety index to quantify the impact of the economic and epidemic developments on the power load. Then, the maximal information coefficient is used to analyze the strong correlation factors of power load during the epidemic and introduce epidemic load correlation characteristics. Finally, meteorological data, historical load and the constructed epidemic correlation features are combined as the input variables of the prediction model, and the prediction algorithm is analyzed by multiple machine learning models. The results show that the load forecasting model with the introduction of the epidemic correlation features can effectively improve the accuracy of load forecasting during the epidemic. © 2022 Power System Protection and Control Press. All rights reserved.
anxiety index; COVID-19 outbreak; load forecasting; machine learning; silent index; Electric power plant loads; Electric power system protection; Epidemiology; Meteorology; Weather forecasting; Correlation features; Machine-learning; On-machines; Power load; Power load forecasting; Short term load forecasting; COVID-19
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
Chinese
Journal:
Power System Protection and Control
Year:
2022
Document Type:
Article
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