On the public citation graph

A Co-Simulation Methodology for Integrating Data-Driven Thermal Sensation Models with Building Energy Control

https://doi.org/10.2139/ssrn.5459080
0 signed · 32 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/bf01450660openalex-snapshot
Experimental evaluation of standard effective temperature a new biometeorological index of man's thermal discomfort
inferredcites →https://doi.org/10.1016/j.adapen.2023.100123openalex-snapshot
Interpretable machine learning for building energy management: A state-of-the-art review
inferredcites →https://doi.org/10.1016/j.apenergy.2021.116452openalex-snapshot
A review of machine learning in building load prediction
inferredcites →https://doi.org/10.1016/j.buildenv.2018.03.053openalex-snapshot
Review of adaptive thermal comfort models in built environmental regulatory documents
inferredcites →https://doi.org/10.1016/j.buildenv.2018.06.022openalex-snapshot
Development of the ASHRAE Global Thermal Comfort Database II
inferredcites →https://doi.org/10.1016/j.buildenv.2021.108737openalex-snapshot
Using Co-simulation between EnergyPlus and CONTAM to evaluate recirculation-based, demand-controlled ventilation strategies in an office building
inferredcites →https://doi.org/10.1016/j.buildenv.2022.109020openalex-snapshot
Evolution and performance analysis of adaptive thermal comfort models – A comprehensive literature review
inferredcites →https://doi.org/10.1016/j.buildenv.2023.110149openalex-snapshot
Physics-informed neural networks for building thermal modeling and demand response control
inferredcites →https://doi.org/10.1016/j.buildenv.2024.112035openalex-snapshot
Deciphering the nonlinear and synergistic role of building energy variables in shaping carbon emissions: A LightGBM- SHAP framework in office buildings
inferredcites →https://doi.org/10.1016/j.buildenv.2025.113045openalex-snapshot
Review of optimization control methods for HVAC systems in Demand Response (DR): Transition from model-driven to model-free approaches and challenges
inferredcites →https://doi.org/10.1016/j.buildenv.2025.113588openalex-snapshot
Physics-Informed Neural Networks for robust thermal comfort prediction: Overcoming data quality limitations through physiological constraints
inferredcites →https://doi.org/10.1016/j.cma.2022.115664openalex-snapshot
PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations
inferredcites →https://doi.org/10.1016/j.csite.2024.105737openalex-snapshot
Influence of transient conditions on thermal comfort evaluations: Field study in university buildings
inferredcites →https://doi.org/10.1016/j.egypro.2017.09.658openalex-snapshot
Fanger’s model of thermal comfort: a model suitable just for men?
inferredcites →https://doi.org/10.1016/j.enbuild.2007.03.007openalex-snapshot
A review on buildings energy consumption information
inferredcites →https://doi.org/10.1016/j.enbuild.2020.110523openalex-snapshot
Efficient PMV computation for public environments with transient populations
inferredcites →https://doi.org/10.1016/j.enbuild.2021.110992openalex-snapshot
Physics-constrained deep learning of multi-zone building thermal dynamics
inferredcites →https://doi.org/10.1016/j.enbuild.2024.115075openalex-snapshot
Sinergym – A virtual testbed for building energy optimization with Reinforcement Learning
inferredcites →https://doi.org/10.1016/j.rineng.2024.102148openalex-snapshot
Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings
inferredcites →https://doi.org/10.1016/j.simpat.2023.102770openalex-snapshot
Co-simulation for buildings and smart energy systems — A taxonomic review
inferredcites →https://doi.org/10.1016/s0003-6870(72)80074-7openalex-snapshot
Thermal comfort: Analysis and applications in environmental engineering
inferredcites →https://doi.org/10.1016/s0378-7788(00)00114-6openalex-snapshot
EnergyPlus: creating a new-generation building energy simulation program
inferredcites →https://doi.org/10.1080/19401490903051959openalex-snapshot
Co-simulation of innovative integrated HVAC systems in buildings
inferredcites →https://doi.org/10.1080/19401493.2013.808265openalex-snapshot
Functional mock-up unit for co-simulation import in EnergyPlus
inferredcites →https://doi.org/10.1145/3061639.3062224openalex-snapshot
Deep Reinforcement Learning for Building HVAC Control
inferredcites →https://doi.org/10.1145/3486611.3488729openalex-snapshot
Sinergym
inferredcites →https://doi.org/10.1145/3600100.3623742openalex-snapshot
CLUE: Safe Model-Based RL HVAC Control Using Epistemic Uncertainty Estimation
inferredcites →https://doi.org/10.1177/1420326x14527975openalex-snapshot
Use of mean skin temperature in evaluation of individual thermal comfort for a person in a sleeping posture under steady thermal environment
inferredcites →https://doi.org/10.3390/buildings12050539openalex-snapshot
Comparison of Model Complexities in Optimal Control Tested in a Real Thermally Activated Building System
inferredcites →https://doi.org/10.3390/buildings13020314openalex-snapshot
Bayesian Optimization Framework for HVAC System Control
inferredcites →https://doi.org/10.3390/en16031524openalex-snapshot
A Review on Adaptive Thermal Comfort of Office Building for Energy-Saving Building Design
inferredcites →https://doi.org/10.3390/life11070681openalex-snapshot
Perception of Thermal Comfort during Skin Cooling and Heating
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.