Ultra-short term power load forecasting based on CEEMDAN-SE and LSTM neural network-Reference-Cited by-同舟云学术

Ultra-short term power load forecasting based on CEEMDAN-SE and LSTM neural network

Published:2023-01 Issue: Volume:279 Page:112666
ISSN:0378-7788
Container-title:Energy and Buildings
language:en
Short-container-title:Energy and Buildings

Author:

Li Ke,Huang Wei,Hu Gaoyuan,Li Jiao

Funder

Hunan Normal University

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Building and Construction,Civil and Structural Engineering

Reference35 articles.

1. Neural network model ensembles for building-level electricity load forecasts;Jetcheva;Energy Build.,2014

2. Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem;Lebotsa;Appl. Energy,2018

3. BACnet-EnOcean smart grid gateway and its application to demand response in buildings;Li;Energy Build.,2021

4. Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings;Yan;Energy Build.,2016

5. Bus load forecasting model selection and variable weights combination forecasting based on forecasting effectiveness and Markov chain-cloud model;Yin;Electric Power Autom. Equip.,2015

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