Transfer learning-based adaptive recursive neural network for short-term non-stationary building heating load prediction-Reference-Cited by-同舟云学术

Transfer learning-based adaptive recursive neural network for short-term non-stationary building heating load prediction

Published:2023-10 Issue: Volume:76 Page:107271
ISSN:2352-7102
Container-title:Journal of Building Engineering
language:en
Short-container-title:Journal of Building Engineering

Author:

Zhou Yong^ORCID,Li Xiang,Liu Yanfeng,Wei Renshu

Publisher

Elsevier BV

Subject

Mechanics of Materials,Safety, Risk, Reliability and Quality,Building and Construction,Architecture,Civil and Structural Engineering

Reference53 articles.

1. Building energy prediction using artificial neural networks: a literature survey;Lu;Energy Build.,2022

2. Elastic weight consolidation-based adaptive neural networks for dynamic building energy load prediction modeling;Zhou;Energy Build.,2022

3. Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques;Olu-Ajayi;J. Build. Eng.,2022

4. Identifying the most significant input parameters for predicting district heating load using an association rule algorithm;Liu;J. Clean. Prod.,2020

5. Sequence-to-sequence deep learning model for building energy consumption prediction with dynamic simulation modeling;Kim;J. Build. Eng.,2021

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