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- W4313537481 endingPage "120602" @default.
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- W4313537481 abstract "Long-term Load Forecasting (LTLF) plays a vital role in the planning of electric utilities. In the long run, utilities face various uncertainties caused by economic and environmental factors. These uncertainties have made LTLF more complex and inaccurate, thus, amplifying financial risks of utilities. A potent contributor to such losses in LTLF accuracy is economic shocks. This study proposes two probabilistic Time-Varying (TV) approaches to capture such shocks in LTLF and minimise accuracy loss, namely TV-XGB-X and TV-PR, and their combinations considering economic policy uncertainty. Both eXtreme Gradient Boosting (XGB) and Polynomial Regression (PR) are extended to include the long-run TV effects of economic shocks as eXogeneous predictors in this paper. These models and their combinations are compared with various non-TV approaches through experiments on the monthly electricity consumption of eight energy-intensive states in the United States. The results reveal that the proposed combined approaches outperform stand-alone models on all datasets. The findings of this study can help utilities in hedging financial risks under shocks." @default.
- W4313537481 created "2023-01-06" @default.
- W4313537481 creator A5007186757 @default.
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- W4313537481 date "2023-03-01" @default.
- W4313537481 modified "2023-10-18" @default.
- W4313537481 title "Time-Varying approaches for Long-Term Electric Load Forecasting under economic shocks" @default.
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- W4313537481 doi "https://doi.org/10.1016/j.apenergy.2022.120602" @default.
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