Recurrent inception convolution neural network for multi short-term load forecasting-Reference-Cited by-同舟云学术

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Recurrent inception convolution neural network for multi short-term load forecasting

Published:2019-07 Issue: Volume:194 Page:328-341
ISSN:0378-7788
Container-title:Energy and Buildings
language:en
Short-container-title:Energy and Buildings

Author:

Kim Junhong,Moon Jihoon,Hwang Eenjun,Kang Pilsung

Funder

National Research Foundation of Korea (NRF)

Ministry of Education

Korea Electric Power Corporation

Publisher

Elsevier BV

Subject

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

Reference79 articles.

1. Operation strategy of multi-energy storage system for ancillary services;Kim;IEEE Trans. Power Syst.,2017

2. Hedging the risk of increased emissions in long term energy planning;Niet;Energy Strat. Rev.,2017

3. Short-term building energy model recommendation system: a meta-learning approach;Cui;Appl. Energy,2016

4. Trees vs Neurons: comparison between random forest and ANN for high-resolution prediction of building energy consumption;Ahmad;Energy Build.,2017

5. Relative efficiency of energy technologies in the Korean mid-term strategic energy technology development plan;Lee;Renew. Sustain. Energy Rev.,2018

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