Benchmarking explanatory models for inertia forecasting using public data of the nordic area
Author(s)
Graham, Jemima
Heylen, evelyn
bian, yuankai
Teng, fei
Type
Conference Paper
Abstract
This paper investigates the performance of a day-ahead explanatory model for inertia forecasting based on field data in the Nordic system, which achieves a 43% reduction in mean absolute percentage error (MAPE) against a state-of-the-art time-series forecast model. The generalizability of the explanatory model is verified by its consistent performance on Nordic and Great Britain datasets. Also, it appears that a long duration of training data is not required to obtain accurate results with this model, but taking a more spatially granular approach reduces the MAPE by 3.6%. Finally, two further model enhancements are studied considering the specific features in Nordic system: (i) a monthly interaction variable applied to the day-ahead national demand forecast feature, reducing the MAPE by up to 18%; and (ii) a feature based on the inertia from hydropower, although this has a negligible impact. The field dataset used for benchmarking is also made publicly available.
Date Issued
2022-07-04
Date Acceptance
2022-03-24
Citation
2022, pp.1-6
Publisher
IEEE
Start Page
1
End Page
6
Copyright Statement
Copyright © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
https://ieeexplore.ieee.org/document/9810572
Source
2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
Publication Status
Published
Start Date
2022-06-12
Finish Date
2022-06-15
Date Publish Online
2022-07-04