Data-driven short term load forecasting with deep neural networks: Unlocking insights for sustainable energy management-Reference-Cited by-同舟云学术

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Data-driven short term load forecasting with deep neural networks: Unlocking insights for sustainable energy management

Published:2024-07 Issue: Volume:232 Page:110376
ISSN:0378-7796
Container-title:Electric Power Systems Research
language:en
Short-container-title:Electric Power Systems Research

Author:

Waheed Waqar^ORCID,Xu Qingshan

Publisher

Elsevier BV

Reference59 articles.

1. A multi-level multi-objective strategy for eco-environmental management of electricity market among micro-grids under high penetration of smart homes, plug-in electric vehicles and energy storage devices;Fatemi;J. Energy Storage,2023

2. A fully robust home energy management model considering real time price and on-board vehicle batteries;Tostado-Véliz;J. Energy Storage,2023

3. R. Weron, Modeling and Forecasting Electricity Loads, in: Modeling and Forecasting Electricity Loads and Prices, 2006, pp. 67–100.

4. A review of short term load forecasting using artificial neural network models;Baliyan;Procedia Comput. Sci.,2015

5. Methods and models for electric load forecasting: a comprehensive review;Hammad;Logist. Sustain. Transp.,2020

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Development of an integrated energy management system for off-grid solar applications with advanced solar forecasting, time-of-use tariffs, and direct load control;Sustainable Energy, Grids and Networks;2024-09

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