Back to Search
Start Over
Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models.
- Source :
- Scientific Reports; 11/18/2023, Vol. 13 Issue 1, p1-15, 15p
- Publication Year :
- 2023
-
Abstract
- This paper focuses on day-ahead electricity load forecasting for substations of the distribution network in France; therefore, the corresponding problem lies between the instability of a single consumption and the stability of a countrywide total demand. Moreover, this problem requires to forecast the loads of over one thousand substations; consequently, it belongs to the field of multiple time series forecasting. To that end, the paper applies an adaptive methodology that provided excellent results at a national scale; the idea is to combine generalized additive models with state-space representations. However, extending this methodology to the prediction of over a thousand time series raises a computational issue. It is solved by developing a frugal variant that reduces the number of estimated parameters: forecasting models are estimated only for a few time series and transfer learning is achieved by relying on aggregation of experts. This approach yields a reduction of computational needs and their associated emissions. Several variants are built, corresponding to different levels of parameter transfer, to find the best trade-off between accuracy and frugality. The selected method achieves competitive results compared to individual models. Finally, the paper highlights the interpretability of the models, which is important for operational applications. [ABSTRACT FROM AUTHOR]
- Subjects :
- TIME series analysis
ELECTRICITY
THRIFTINESS
DEMAND forecasting
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 13
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Scientific Reports
- Publication Type :
- Academic Journal
- Accession number :
- 173738304
- Full Text :
- https://doi.org/10.1038/s41598-023-42488-1