A global modeling framework for load forecasting in distribution networks

Grabner, Miha (avtor), Wang, Yi (avtor), Wen, Qingsong (avtor), Blažič, Boštjan (avtor), Štruc, Vitomir (avtor)

With the increasing numbers of smart meter installations, scalable and efficient load forecasting techniques are critically needed to ensure sustainable situation awareness within the distribution networks. Distribution networks include a large amount of different loads at various aggregation levels, such as individual consumers, low-voltage feeders, and transformer stations. It is impractical to develop individual (or so-called local) forecasting models for each load separately. Additionally, such local models also (i) (largely) ignore the strong dependencies between different loads that might be present due to their spatial proximity and the characteristics of the distribution network, (ii) require historical data for each load to be able to make forecasts, and (iii) are incapable of adjusting to changes in the load behavior without retraining. To address these issues, we propose a global modeling framework for load forecasting in distribution networks that, unlike its local competitors, relies on a single global model to generate forecasts for a large number of loads. The global nature of the framework, significantly reduces the computational burden typically required when training multiple local forecasting models, efficiently exploits the cross-series information shared among different loads, and facilitates forecasts even when historical data for a load is missing or the behavior of a load evolves over time. To further improve on the performance of the proposed framework, an unsupervised localization mechanism and optimal ensemble construction strategy are also proposed to localize/personalize the global forecasting model to different load characteristics. Our experimental results show that the proposed framework outperforms naive benchmarks by more than 25% (in terms of Mean Absolute Error) on real-world dataset while exhibiting highly desirable characteristics when compared to the local models that are predominantly used in the literature. All source code and data are made publicly available to enable reproducibility: https://github.com/mihagrabner/GlobalModelingFramework.

• Jezik: Angleški jezik
• Ključne besede: load forecasting, distribution networks, predictive models, smart meter, global model, deep learning
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FE - Fakulteta za elektrotehniko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2023
• Št. strani: Str. 4927–4941
• Številčenje: Vol. 14, no. 6
• PID: 20.500.12556/RUL-154010
• UDK: 621.31
• ISSN pri članku: 1949-3053
• DOI: 10.1109/TSG.2023.3264525
• COBISS.SI-ID: 148068611
• Datum objave v RUL: 18.01.2024

GRABNER, Miha, WANG, Yi, WEN, Qingsong, BLAŽIČ, Boštjan in ŠTRUC, Vitomir, 2023, A global modeling framework for load forecasting in distribution networks. IEEE transactions on smart grid [na spletu]. 2023. Vol. 14, no. 6. [Dostopano 5 maj 2025]. DOI 10.1109/TSG.2023.3264525. Pridobljeno s: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=slv&id=154010

Gradivo je del revije

• Naslov: IEEE transactions on smart grid
• Skrajšan naslov: IEEE trans. smart grid
• Založnik: Institute of Electrical and Electronics Engineers
• ISSN: 1949-3053
• COBISS.SI-ID: 7809108

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: napovedovanje odjema, distribucijsko omrežje, modeli za napovedovanje