End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting

Rushil  Vohra; Ali Rajaei; Jochen L. Cremer

doi:10.1109/PowerTech55446.2023.10202904

End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting

Rushil Vohra, Ali Rajaei, Jochen L. Cremer

Intelligent Electrical Power Grids

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

7 Downloads (Pure)

Abstract

With the increasing penetration of renewable power sources such as wind and solar, accurate short-term, nowcasting renewable power prediction is becoming increasingly important. This paper investigates the multi-modal (MM) learning and end-to-end (E2E) learning for nowcasting renewable power as an intermediate to energy management systems. MM combines features from all-sky imagery and meteorological sensor data as two modalities to predict renewable power generation that otherwise could not be combined effectively. The combined, predicted values are then input to a differentiable optimal power flow (OPF) formulation simulating the energy management. For the first time, MM is combined with E2E training of the model that minimises the expected total system cost. The case study tests the proposed methodology on the real sky and meteorological data from the Netherlands. In our study, the proposed MM- E2E model reduced system cost by 30% compared to uni-modal baselines.

Original language	English
Title of host publication	Proceedings of the 2023 IEEE Belgrade PowerTech
Place of Publication	Piscataway
Publisher	IEEE
Pages	1-8
Number of pages	8
ISBN (Electronic)	978-1-6654-8778-8
ISBN (Print)	978-1-6654-8779-5
DOIs	https://doi.org/10.1109/PowerTech55446.2023.10202904
Publication status	Published - 2023
Event	2023 IEEE Belgrade PowerTech - Belgrade, Serbia Duration: 25 Jun 2023 → 29 Jun 2023

Publication series

Name	2023 IEEE Belgrade PowerTech, PowerTech 2023

Conference

Conference	2023 IEEE Belgrade PowerTech
Country/Territory	Serbia
City	Belgrade
Period	25/06/23 → 29/06/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

multi-modal learning
end-to-end learning
optimal power flow
power forecasting

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/PowerTech55446.2023.10202904

End-to-End_Learning_with_Multiple_Modalities_for_System-Optimised_Renewables_NowcastingFinal published version, 1.17 MB

Cite this

@inproceedings{951d7cf7ee9a4db59a530ad8df70b95b,

title = "End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting",

abstract = "With the increasing penetration of renewable power sources such as wind and solar, accurate short-term, nowcasting renewable power prediction is becoming increasingly important. This paper investigates the multi-modal (MM) learning and end-to-end (E2E) learning for nowcasting renewable power as an intermediate to energy management systems. MM combines features from all-sky imagery and meteorological sensor data as two modalities to predict renewable power generation that otherwise could not be combined effectively. The combined, predicted values are then input to a differentiable optimal power flow (OPF) formulation simulating the energy management. For the first time, MM is combined with E2E training of the model that minimises the expected total system cost. The case study tests the proposed methodology on the real sky and meteorological data from the Netherlands. In our study, the proposed MM- E2E model reduced system cost by 30% compared to uni-modal baselines.",

keywords = "multi-modal learning, end-to-end learning, optimal power flow, power forecasting",

author = "Rushil Vohra and Ali Rajaei and Cremer, {Jochen L.}",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 2023 IEEE Belgrade PowerTech ; Conference date: 25-06-2023 Through 29-06-2023",

year = "2023",

doi = "10.1109/PowerTech55446.2023.10202904",

language = "English",

isbn = "978-1-6654-8779-5",

series = "2023 IEEE Belgrade PowerTech, PowerTech 2023",

publisher = "IEEE",

pages = "1--8",

booktitle = "Proceedings of the 2023 IEEE Belgrade PowerTech",

address = "United States",

}

End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting. / Vohra, Rushil ; Rajaei, Ali ; Cremer, Jochen L.
Proceedings of the 2023 IEEE Belgrade PowerTech. Piscataway: IEEE, 2023. p. 1-8 (2023 IEEE Belgrade PowerTech, PowerTech 2023).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting

AU - Vohra, Rushil

AU - Rajaei, Ali

AU - Cremer, Jochen L.

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2023

Y1 - 2023

N2 - With the increasing penetration of renewable power sources such as wind and solar, accurate short-term, nowcasting renewable power prediction is becoming increasingly important. This paper investigates the multi-modal (MM) learning and end-to-end (E2E) learning for nowcasting renewable power as an intermediate to energy management systems. MM combines features from all-sky imagery and meteorological sensor data as two modalities to predict renewable power generation that otherwise could not be combined effectively. The combined, predicted values are then input to a differentiable optimal power flow (OPF) formulation simulating the energy management. For the first time, MM is combined with E2E training of the model that minimises the expected total system cost. The case study tests the proposed methodology on the real sky and meteorological data from the Netherlands. In our study, the proposed MM- E2E model reduced system cost by 30% compared to uni-modal baselines.

AB - With the increasing penetration of renewable power sources such as wind and solar, accurate short-term, nowcasting renewable power prediction is becoming increasingly important. This paper investigates the multi-modal (MM) learning and end-to-end (E2E) learning for nowcasting renewable power as an intermediate to energy management systems. MM combines features from all-sky imagery and meteorological sensor data as two modalities to predict renewable power generation that otherwise could not be combined effectively. The combined, predicted values are then input to a differentiable optimal power flow (OPF) formulation simulating the energy management. For the first time, MM is combined with E2E training of the model that minimises the expected total system cost. The case study tests the proposed methodology on the real sky and meteorological data from the Netherlands. In our study, the proposed MM- E2E model reduced system cost by 30% compared to uni-modal baselines.

KW - multi-modal learning

KW - end-to-end learning

KW - optimal power flow

KW - power forecasting

UR - http://www.scopus.com/inward/record.url?scp=85152145411&partnerID=8YFLogxK

U2 - 10.1109/PowerTech55446.2023.10202904

DO - 10.1109/PowerTech55446.2023.10202904

M3 - Conference contribution

SN - 978-1-6654-8779-5

T3 - 2023 IEEE Belgrade PowerTech, PowerTech 2023

SP - 1

EP - 8

BT - Proceedings of the 2023 IEEE Belgrade PowerTech

PB - IEEE

CY - Piscataway

T2 - 2023 IEEE Belgrade PowerTech

Y2 - 25 June 2023 through 29 June 2023

ER -

End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this