Development of deep learning artificial neural networks models to predict temperature and power demand variation for demand response application in cold storage

Author:

Hoang H.M.ORCID,Akerma M.,Mellouli N.,Montagner A. Le,Leducq D.,Delahaye A.

Funder

ADEME

Publisher

Elsevier BV

Subject

Mechanical Engineering,Building and Construction

Reference41 articles.

1. Renewable Energy Directive (RED II), Directive (EU) 2018 /2001 (recast) on the promotion of the use of energy from renewable sources.

2. Demand response for variable renewable energy integration: A proposed approach and its impacts;McPherson;Energy,2020

3. Demand-side improvement of short-term load forecasting using a proactive load management – a supermarket use case;Glavan;Energy Build.,2019

4. Benefits and challenges of electrical demand response: A critical review;O׳Connell;Renew. Sustainable Energy Rev.,2014

5. Cold chain technology brief: cold storage and refrigerated warehouse,2018

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