Pregled bibliografske jedinice broj: 1257657
On the Value of Distribution Network Topology Information in the Identification of End-user Phase Consumption: A Graph Neural Network Approach
On the Value of Distribution Network Topology Information in the Identification of End-user Phase Consumption: A Graph Neural Network Approach // CEN2022: Applied Energy Symposium
Ningbo, Kina: Applied Energy Innovation Institute (AEii), 2023. str. 1-5 doi:10.46855/energy-proceedings-10407 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
On the Value of Distribution Network Topology
Information in the Identification of End-user
Phase Consumption: A Graph Neural Network Approach
Autori
Matijašević, Terezija ; Antić, Tomislav ; Capuder, Tomislav
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Skup
CEN2022: Applied Energy Symposium
Mjesto i datum
Ningbo, Kina, 23.04.2022. - 25.04.2022
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
smart meters ; distribution network ; phase consumption ; machine learning ; graph neural network
Sažetak
End-users are transiting towards more active, integrating new low-carbon (LC) technologies and bringing unpredictability to low-voltage (LV) distribution networks. Although smart meters have a great potential in increasing the observability, they are mostly being employed only for billing purposes, leaving many other possibilities unexploited, further complicating the many analyses required for effective operational planning and real-time (RT) operation. Detection of phase consumption of end-users is significantly difficult, due to the nonlinear relationships between obtained phase voltage measurements and aggregated end-user consumption. Machine learning (ML) is increasingly used for these and similar problems, and therefore, in this paper, a neural network (NN) – based model is developed to detect end-user consumption in an LV distribution network from available voltage measurements and aggregated end-user consumption. Furthermore, the influence of topology on the output values of the model is investigated and a graph neural network (GNN) – based model is created that considers both the structure and data of the distribution network elements. Both models are tested on the real-world LV distribution network with more than 150 end- users. The results showed the effectiveness of both models in determining the distribution of end-user consumption, with the GNN-based model showing significantly better results. Such a model can help the energy utilities to overcome this time-consuming problem and lay a good foundation for further analyzes required to enable operation and planning of distribution networks.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo
POVEZANOST RADA
