Deep learning modeling in electricity load forecasting: Improved accuracy by combining DWT and LSTM-Reference-Cited by-同舟云学术

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Deep learning modeling in electricity load forecasting: Improved accuracy by combining DWT and LSTM

Published:2024-12 Issue: Volume:12 Page:2873-2900
ISSN:2352-4847
Container-title:Energy Reports
language:en
Short-container-title:Energy Reports

Author:

Nabavi Seyed Azad^ORCID,Mohammadi Sahar,Motlagh Naser Hossein^ORCID,Tarkoma Sasu^ORCID,Geyer Philipp

Publisher

Elsevier BV

Reference95 articles.

1. Substation mid-term electric load forecasting by knowledge-based method;Abdolrezaei;Energy Ecol. Environ.,2021

2. State-of-the-art in artificial neural network applications: A survey;Abiodun;Heliyon,2018

3. A deep learning model for electricity demand forecasting based on a tropical data;Adewuyi;Appl. Comput. Sci.,2020

4. Load forecasting techniques for power system: Research challenges and survey;Ahmad;IEEE Access,2022

5. Sleep characteristics of Iranian people and their effects on daytime functioning: a population-based study;Akbarpour;Sci. Rep.,2022

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