Reference health
Energy optimization associated with thermal comfort and indoor air control via a deep reinforcement learning algorithm
15/15 checkable references clean · checked 2026-07-22
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
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The 15 checked references that resolve
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The Non-Linear Relationship between Electricity Consumption and Temperature in Taiwan: An Application for STR (Smooth Transition Regression) Model
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an Indoor Pollutant? Direct Effects of Low-to-Moderate CO
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Concentrations on Human Decision-Making Performance
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Modeling techniques used in building HVAC control systems: A review
resolves10.1016/j.buildenv.2013.11.016
Theory and applications of HVAC control systems – A review of model predictive control (MPC)
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Advanced control systems engineering for energy and comfort management in a building environment—A review
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Predicting of fan speed for energy saving in HVAC system based on adaptive network based fuzzy inference system
resolves10.1016/j.apenergy.2013.10.036
Thermal comfort and energy consumption in modern versus traditional buildings in Cameroon: A questionnaire-based statistical study
resolves10.1016/j.enbuild.2016.05.067
Satisfaction based Q-learning for integrated lighting and blind control
resolves10.1016/j.enbuild.2017.08.052
Model-free control of thermostatically controlled loads connected to a district heating network
resolves10.1016/j.buildenv.2006.07.010
Reinforcement learning for energy conservation and comfort in buildings
resolves10.3233/AIS-140288
Using reinforcement learning to optimize occupant comfort and energy usage in HVAC systems
resolves10.1016/S0378-7788(00)00114-6
EnergyPlus: creating a new-generation building energy simulation program
resolves10.1080/19401493.2010.518631
Co-simulation of building energy and control systems with the Building Controls Virtual Test Bed
The 32 references without a DOI — listed, not checked
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib1
no DOI — not checkedEnergy consumption analysis for concrete residences—a baseline study in Taiwan
no DOI — not checkedComfort temperature and preferred temperature in Taiwan
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib4
no DOI — not checkedOccupants' behavior in Taiwan
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib7
no DOI — not checkedIndoor environment in schools–pupils health and performance in regard to CO2 concentrations
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib10
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib11
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib12
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib13
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib14
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib15
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib16
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib17
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib18
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib19
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib25
no DOI — not checkedReinforcement learning and Markov decision processes
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib28
no DOI — not checkedInvestigation of reinforcement learning for building thermal mass control
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib34
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib35
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib36
no DOI — not checkedDeep reinforcement learning for building HVAC control
no DOI — not checkedA deep reinforcement learning approach to using whole building energy model for HVAC optimal control
no DOI — not checkedDeep reinforcement learning with double Q-Learning
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib40
no DOI — not checkedTensorFlow: a system for large-scale machine learning
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib43
no DOI — not checkedOpenStudio: an open source integrated analysis platform
no DOI — not checked10.1016/j.buildenv.2019.03.038_bib46
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