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SCADA-agnostic Power Modelling for Distributed Renewable Energy Sources

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
Publication date9/10/2020
Host publication2020 IEEE 21st International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)
PublisherIEEE
Pages379-384
Number of pages6
ISBN (electronic)9781728173740, 9781728173757
<mark>Original language</mark>English
Event21st IEEE INTERNATIONAL SYMPOSIUM ON A WORLD OF WIRELESS, MOBILE AND MULTIMEDIA NETWORKS
(IEEE WOWMOM 2020)
- Cork, Ireland
Duration: 31/08/20203/09/2020
http://www.cs.ucc.ie/wowmom2020/

Conference

Conference21st IEEE INTERNATIONAL SYMPOSIUM ON A WORLD OF WIRELESS, MOBILE AND MULTIMEDIA NETWORKS
(IEEE WOWMOM 2020)
Abbreviated titleIEEE WOWMOM 2020
Country/TerritoryIreland
CityCork
Period31/08/203/09/20
Internet address

Conference

Conference21st IEEE INTERNATIONAL SYMPOSIUM ON A WORLD OF WIRELESS, MOBILE AND MULTIMEDIA NETWORKS
(IEEE WOWMOM 2020)
Abbreviated titleIEEE WOWMOM 2020
Country/TerritoryIreland
CityCork
Period31/08/203/09/20
Internet address

Abstract

Distributed Renewable Energy Sources (DRES) are considered as instrumental within modern smart grids and more broadly to the various ancillary services contained within the energy trading market. Thus, the adequate power production profiling and forecasting of DRES deployments is of vital importance such as to support various grid optimisation and accounting processes. The variety of DRES in stallation companies in conjunction with the diversity of ownership on DRES machinery, controller firmware and Supervisory Control and Data Acquisition (SCADA) software leads to cases where centralised SCADA measurements are not entirely available or are provided under a subscription-based model. In this work, we consider this pragmatic scenario and introduce a SCADA-agnostic approach that utilises freely available weather measurements for explicitly profiling and forecasting power generation as produced in real wind turbine deployments. For this purpose, we leverage various machine learning (ML) libraries to demonstrate the applicability of our system and further compare it with forecasting outputs obtained when using SCADA measurements. Through this study, we demonstrate a viable and exogenous profiling solution achieving similar accuracy with SCADA-based schemes under much lower computational costs.