Predicting hourly heating load in residential buildings using a hybrid SSA–CNN

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Predicting hourly heating load in residential buildings using a hybrid SSA–CNN–SVM approach

Published:2024-07 Issue: Volume:59 Page:104516
ISSN:2214-157X
Container-title:Case Studies in Thermal Engineering
language:en
Short-container-title:Case Studies in Thermal Engineering

Author:

An Wenhan,Gao Bo,Liu Jianhua,Ni Ji,Liu Jiying^ORCID

Publisher

Elsevier BV

Reference41 articles.

1. Experimental investigation on an energy-efficient floor heating system with intelligent control: a case study in Chengdu, China;Jiang;Case Stud. Therm. Eng.,2021

2. Modeling heating and cooling loads by artificial intelligence for energy-efficient building design;Chou;Energy Build.,2014

3. Heating load prediction of residential district using hybrid model based on CNN;Zhao;Energy Build.,2022

4. An improved input variable selection method of the data-driven model for building heating load prediction;Ling;J. Build. Eng.,2021

5. Model input selection for building heating load prediction: a case study for an office building in Tianjin;Ding;Energy Build.,2018

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