A Global Modeling Framework for Load Forecasting in Distribution Networks-Reference-Cited by-同舟云学术

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A Global Modeling Framework for Load Forecasting in Distribution Networks

Published:2023-11 Issue:6 Volume:14 Page:4927-4941
ISSN:1949-3053
Container-title:IEEE Transactions on Smart Grid
language:
Short-container-title:IEEE Trans. Smart Grid

Author:

Grabner Miha¹^ORCID,Wang Yi²^ORCID,Wen Qingsong³,Blažič Boštjan¹,Štruc Vitomir¹^ORCID

Affiliation:

1. Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia

2. Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, SAR, China

3. DAMO Academy, Alibaba Group (U.S.) Inc., Bellevue, WA, USA

Funder

ARRS Research Program P2-0250 (B) “Metrology and Biometric Systems”

ARRS Research Project DRIFT

Alibaba Innovative Research Programme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/5165411/10288469/10092804.pdf?arnumber=10092804

Reference34 articles.

1. A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting

2. DeepAR: Probabilistic forecasting with autoregressive recurrent networks

3. N-BEATS neural network for mid-term electricity load forecasting

4. Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

5. Probabilistic forecasting with temporal convolutional neural network

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4. A Comprehensive Review of Behind-the-Meter Distributed Energy Resources Load Forecasting: Models, Challenges, and Emerging Technologies;Energies;2024-05-24

5. From Time-Series to Hybrid Models: Advancements in Short-Term Load Forecasting Embracing Smart Grid Paradigm;Applied Sciences;2024-05-23

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