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Lookup NU author(s): Dr Marcos Santos,
Dr Neal WadeORCiD,
Dr David Greenwood
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by IET, 2021.
For re-use rights please refer to the publisher's terms and conditions.
Modern electricity networks are increasing in complexity due to the integration of variable renewable energy sources at both transmission and distribution levels. As part of the daily operation and scheduling of such systems, the very short to medium-term forecasts are a crucial element, providing the expected demand and generation output over a requested period. Artificial neural networks techniques promise to deliver adaptability which overcome the source's variability if the inputs are properly selected. This paper proposes demand and generation forecasting models for distribution systems, each employing a different artificial neural networks architecture. The models are demonstrated on real data from Energie Güssing distribution system. The adaptability of the models to changes in demand during the Covid-19 lockdown is investigated.
Author(s): Santos M, Huo D, Resch M, Wade N, Greenwood D
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 26th International Conference and Exhibition on Electricity Distribution (CIRED 2021)
Year of Conference: 2021
Online publication date: 25/01/2022
Acceptance date: 05/05/2021
Date deposited: 23/03/2022
Library holdings: Search Newcastle University Library for this item