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Titel: Deep Learning-based Power Load Shedding Approach for Gaza's Electricity Grid
VerfasserIn: El Astal, Mohammed Taha
Elhabbash, Alaa
Abu-Hudrouss, Ammar
Frey, Georg
HerausgeberIn: Leonowicz, Zbigniew
Sprache: Englisch
Titel: Conference Proceedings 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe) : 28 June-1 July, 2022, Prague, Czech Republic
Verlag/Plattform: IEEE
Erscheinungsjahr: 2022
Erscheinungsort: [Piscataway, NJ]
Konferenzort: Prague, Czech Republic
Freie Schlagwörter: Schedules
Temperature distribution
Wind speed
Estimation
Load shedding
Artificial neural networks
Humidity
DDC-Sachgruppe: 600 Technik
Dokumenttyp: Konferenzbeitrag (in einem Konferenzband / InProceedings erschienener Beitrag)
Abstract: Many developing countries suffer from a chronic electricity deficit. As a particular case, this shortage in electricity supplies in Gaza Strip undermines the already fragile conditions, and is considered as an obstacle to any economic development. In order to distribute the available amount of electricity evenly, the local utility executes plentiful on/off power load shedding daily. However, further complications are experienced because of variations in demands and sources daily. Here, a high-precise prediction model for load demand is proposed. This is to help staff in making quick and accurate decisions. The model is built using an artificial neural network (ANN), and trained based on supervisory control and data acquisition (SCADA)'s data including generation, temperature, humidity, and wind speed. The model can predict the energy consumption for given parameters, and hence identifies the power load shedding schedule needed for the given day. The proposed model was evaluated in terms of accuracy and has proven to result in a good estimation.
DOI der Erstveröffentlichung: 10.1109/EEEIC/ICPSEurope54979.2022.9854713
URL der Erstveröffentlichung: https://ieeexplore.ieee.org/document/9854713
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-380818
hdl:20.500.11880/34637
http://dx.doi.org/10.22028/D291-38081
ISBN: 978-1-6654-8537-1
Datum des Eintrags: 5-Dez-2022
Fakultät: NT - Naturwissenschaftlich- Technische Fakultät
Fachrichtung: NT - Systems Engineering
Professur: NT - Prof. Dr. Georg Frey
Sammlung:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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