Smoothed Bernstein Online Aggregation for Short-Term Load Forecasting in IEEE DataPort Competition on Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm
IEEE Open Access Journal of Power and Energy
; 2022.
Article
in English
| Scopus | ID: covidwho-1779148
ABSTRACT
We present a winning method of the IEEE DataPort Competition on Day-Ahead Electricity Demand Forecasting Post-COVID Paradigm. The day-ahead load forecasting approach is based on a novel online forecast combination of multiple point prediction models. It contains four steps:
i) data cleaning and preprocessing, ii) a new holiday adjustment procedure, iii) training of individual forecasting models, iv) forecast combination by smoothed Bernstein Online Aggregation (BOA). The approach is flexible and can quickly adjust to new energy system situations as they occurred during and after COVID-19 shutdowns. The ensemble of individual prediction models ranges from simple time series models to sophisticated models like generalized additive models (GAMs) and high-dimensional linear models estimated by lasso. They incorporate autoregressive, calendar, and weather effects efficiently. All steps contain novel concepts that contribute to the excellent forecasting performance of the proposed method. It is especially true for the holiday adjustment procedure and the fully adaptive smoothed BOA approach. Author
aggregation method; COVID-19; demand analytics; ensemble; forecast combination; Forecasting; forecasting competition; GAM; generalized additive models; holiday effects; lasso; Load forecasting; Load modeling; lockdown; Meteorology; online learning; post-COVID effects; prediction; Predictive models; shutdown; Task analysis; weather effects; Electric power plant loads; Electric power utilization; Weather forecasting; Aggregation methods; Demand analytic; Forecast combinations; Generalized additive model; Holiday effect; Post-COVID effect; Weather effect; Job analysis
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Topics:
Long Covid
Language:
English
Journal:
IEEE Open Access Journal of Power and Energy
Year:
2022
Document Type:
Article
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