A combined deep learning application for short term load forecasting-Reference-Cited by-同舟云学术

A combined deep learning application for short term load forecasting

Published:2021-08 Issue:4 Volume:60 Page:3807-3818
ISSN:1110-0168
Container-title:Alexandria Engineering Journal
language:en
Short-container-title:Alexandria Engineering Journal

Author:

Ozer Ilyas^ORCID,Efe Serhat Berat,Ozbay Harun

Publisher

Elsevier BV

Subject

General Engineering

Reference47 articles.

1. Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach;Talaat;Energy,2020

2. A proposed intelligent short-term load forecasting hybrid models of ANN, WNN and KF based on clustering techniques for smart grid;Aly;Electr. Power Syst. Res.,2020

3. Smart electricity meter data intelligence for future energy systems: a survey;Alahakoon;IEEE Trans. Ind. Inf.,2016

4. Hour-ahead price based energy management scheme for industrial facilities;Huang;IEEE Trans. Ind. Inf.,2017

5. Short-term industrial reactive power forecasting;Bracale;Int. J. Electric. Power Energy Syst.,2019

Cited by 49 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. A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks;Sustainability;2024-09-07

2. Advances in Deep Learning Techniques for Short-term Energy Load Forecasting Applications: A Review;Archives of Computational Methods in Engineering;2024-06-26

3. Ranking building design and operation parameters for residential heating demand forecasting with machine learning;Journal of Building Engineering;2024-06

4. Boosting short term electric load forecasting of high & medium voltage substations with visibility graphs and graph neural networks;Sustainable Energy, Grids and Networks;2024-06

5. Load Forecasting Using Random Forest Regression Algorithm in Machine Learning;2024 International Conference on Science Technology Engineering and Management (ICSTEM);2024-04-26