An instance based multi-source transfer learning strategy for building’s short-term electricity loads prediction under sparse data scenarios-Reference-Cited by-同舟云学术

An instance based multi-source transfer learning strategy for building’s short-term electricity loads prediction under sparse data scenarios

Published:2024-05 Issue: Volume:85 Page:108713
ISSN:2352-7102
Container-title:Journal of Building Engineering
language:en
Short-container-title:Journal of Building Engineering

Author:

Wei Borui^ORCID,Li Kangji^ORCID,Zhou Shiyi,Xue Wenping^ORCID,Tan Gang

Funder

Six Talent Climax Foundation of Jiangsu

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference44 articles.

1. Building energy model calibration using a surrogate neural network;Herbinger;Energy Build.,2023

2. Automated energy performance certificate based urban building energy modelling approach for predicting heat load profiles of districts;Heidenthaler;Energy,2023

3. A comprehensive framework based on Bayesian optimization and skip connections artificial neural networks to predict buildings energy performance;Abbass;J. Build. Eng.,2023

4. Energy cost and consumption reduction of an office building by chaotic satin bowerbird optimization algorithm with model predictive control and artificial neural network: A case study;Chen;Energy,2023

5. Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks;Behmiri;Energy,2023

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