Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings-Reference-Cited by-同舟云学术

Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings

Published:2020-10 Issue: Volume:224 Page:110225
ISSN:0378-7788
Container-title:Energy and Buildings
language:en
Short-container-title:Energy and Buildings

Author:

Brandi Silvio,Piscitelli Marco Savino,Martellacci Marco,Capozzoli Alfonso

Publisher

Elsevier BV

Subject

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

Reference51 articles.

1. Capozzoli A, Mechri HE, Corrado V. Impacts of architectural design choices on building energy performance applications of uncertainty and sensitivity techniques 2009;15217:1000–7.

2. Extracting knowledge from building-related data - a data mining framework;Yu;Build Simul,2013

3. A framework for knowledge discovery in massive building automation data and its application in building diagnostics;Fan;Autom Constr,2015

4. Mining typical load profiles in buildings to support energy management in the smart city context;Capozzoli;Energy Procedia,2017

5. Recognition and classification of typical load profiles in buildings with non-intrusive learning approach;Piscitelli;Appl Energy,2019

Cited by 137 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Joint optimization for temperature and humidity independent control system based on multi-agent reinforcement learning with cooperative mechanisms;Applied Energy;2024-12

2. Multi-source transfer learning method for enhancing the deployment of deep reinforcement learning in multi-zone building HVAC control;Energy and Buildings;2024-11

3. Deep reinforcement learning for PID parameter tuning in greenhouse HVAC system energy Optimization: A TRNSYS-Python cosimulation approach;Expert Systems with Applications;2024-10

4. A comparative study of DQN and D3QN for HVAC system optimization control;Energy;2024-10

5. Optimizing the thermal performance of phase change materials in building applications using deep reinforcement learning and Bayesian optimization;Thermal Science and Engineering Progress;2024-10