District heater load forecasting based on machine learning and parallel CNN-LSTM attention-Reference-Cited by-同舟云学术

District heater load forecasting based on machine learning and parallel CNN-LSTM attention

Published:2022-05 Issue: Volume:246 Page:123350
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Chung Won Hee,Gu Yeong Hyeon,Yoo Seong Joon^ORCID

Funder

Ministry of Science and ICT, South Korea

Institute for Information and Communications Technology Promotion

Publisher

Elsevier BV

Subject

General Energy,Pollution,Mechanical Engineering,Building and Construction,Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Civil and Structural Engineering

Reference41 articles.

1. Analysis of CO2 emission reduction contribution and efficiency of China's solar photovoltaic industry: based on Input-output perspective;Ren;Energy,2020

2. Electric load forecasting methods: tools for decision making;Hahn;Eur J Oper Res,2009

3. Applied mathematics for restructured electric power systems;Chow,2005

4. Development and validation of regression models to predict monthly heating demand for residential buildings;Catalina;Energy Build,2008

5. Hourly thermal load prediction for the next 24 hours by ARIMA, EWMA, LR and an artificial neural network (No. CONF-950104-);Kawashima,1995

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