Transferability investigation of a Sim2Real deep transfer learning framework for cross-building energy prediction-Reference-Cited by-同舟云学术

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Transferability investigation of a Sim2Real deep transfer learning framework for cross-building energy prediction

Published:2023-05 Issue: Volume:287 Page:112968
ISSN:0378-7788
Container-title:Energy and Buildings
language:en
Short-container-title:Energy and Buildings

Author:

Fang Xi,Gong Guangcai,Li Guannan,Chun Liang,Peng Pei,Shi Xing

Funder

Education Department of Hunan Province

Hunan Provincial Science and Technology Department

Publisher

Elsevier BV

Subject

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

Reference51 articles.

1. Reinforcement learning for building controls: The opportunities and challenges;Wang;Appl. Energy,2020

2. Nationwide savings analysis of energy conservation measures in buildings;Qian;Energ. Conver. Manage.,2019

3. Cross temporal-spatial transferability investigation of deep reinforcement learning control strategy in the building HVAC system level;Fang;Energy,2023

4. A review on renewable energy and electricity requirement forecasting models for smart grid and buildings;Ahmad;Sustain. Cities Soc.,2020

5. Deep reinforcement learning optimal control strategy for temperature setpoint real-time reset in multi-zone building HVAC system;Fang;Appl. Therm. Eng.,2022

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3. Controllable cross-building multi-objective optimisation for NZEBs: A framework Utilising parametric generation and intelligent algorithms;Applied Energy;2024-11

4. A hybrid transfer learning to continual learning strategy for improving cross-building energy prediction in data increment scenario;Journal of Building Engineering;2024-10

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