Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling | Paper Citation Record

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This paper cites Prediction of building power consumption using transfer learning-based reference building and simulation dataset.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Prediction of building power consumption using transfer learning-based reference building and simulation dataset

Reference 1

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This paper cites DigitalTwinforHVACLoadandEnergyStoragebasedona Hybrid ML Model with CTA-2045 Controls Capability, in: 2022 IEEE Energy Conversion Congress and Exposition (ECCE), pp.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling DigitalTwinforHVACLoadandEnergyStoragebasedona Hybrid ML Model with CTA-2045 Controls Capability, in: 2022 IEEE Energy Conversion Congress and Exposition (ECCE), pp

Reference 2

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Observation e6310bae-3cc4-4a50-a7d0-207492435105 · outbound

This paper cites Probabilistic indoor temperature forecasting: A new approach using bernstein- polynomial normalizing flows.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Probabilistic indoor temperature forecasting: A new approach using bernstein- polynomial normalizing flows

Reference 3

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This paper cites Identifying suitable models for the heat dynamics of buildings.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Identifying suitable models for the heat dynamics of buildings

Reference 4

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This paper cites Pattern Recognition and Machine Learning.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Pattern Recognition and Machine Learning

Reference 5

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This paper cites Quantifying Uncertainty with Conformal Prediction for Heating and Cooling Load Forecasting in Building Performance Simulation.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Quantifying Uncertainty with Conformal Prediction for Heating and Cooling Load Forecasting in Building Performance Simulation

Reference 6

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This paper cites Probabilistic electric load forecasting through Bayesian Mixture Density Networks.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Probabilistic electric load forecasting through Bayesian Mixture Density Networks

Reference 7

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Observation 7581c236-4d11-45d2-a436-6902df4a7e89 · outbound

This paper cites Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus

Reference 8

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Observation 91ecabf0-32f8-43eb-a64c-34c211af9569 · outbound

This paper cites A hybrid-model forecasting framework for reducing the building energy performance gap.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling A hybrid-model forecasting framework for reducing the building energy performance gap

Reference 9

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Observation 55678db2-deb5-4bd4-917d-d6ac256f72f0 · outbound

This paper cites Physics-informed neural networks for building thermal modeling and demand response control.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Physics-informed neural networks for building thermal modeling and demand response control

Reference 10

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This paper cites Context-Aware Urban Energy Efficiency Optimization Using Hybrid Physical Models, in: Climate Change AI, Climate Change AI.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Context-Aware Urban Energy Efficiency Optimization Using Hybrid Physical Models, in: Climate Change AI, Climate Change AI

Reference 11

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Observation bfe1c8ad-5f4a-4156-bf99-2ddd48050e2a · outbound

This paper cites Review of data-driven energy modelling techniques for building retrofit.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Review of data-driven energy modelling techniques for building retrofit

Reference 12

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This paper cites CVXPY: A Python-embedded modeling language for convex optimization.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling CVXPY: A Python-embedded modeling language for convex optimization

Reference 13

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This paper cites Machine Learning & Uncertainty Quantification: Application in Building Energy Consumption, in: 2022 Annual Reliability and Maintainability Symposium (RAMS), pp.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Machine Learning & Uncertainty Quantification: Application in Building Energy Consumption, in: 2022 Annual Reliability and Maintainability Symposium (RAMS), pp

Reference 14

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This paper cites Modeling the Thermal Dynamics of Buildings: A Latent- Force-Model-BasedApproach.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Modeling the Thermal Dynamics of Buildings: A Latent- Force-Model-BasedApproach

Reference 15

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This paper cites Adaptive conformal inference under distribution shift.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Adaptive conformal inference under distribution shift

Reference 16

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This paper cites Physics informed neural networks for control oriented thermal modeling of buildings.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Physics informed neural networks for control oriented thermal modeling of buildings

Reference 17

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This paper cites Uncertainty quantification and sensitivity analysis of energy consumption in substation buildings at the planning stage.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Uncertainty quantification and sensitivity analysis of energy consumption in substation buildings at the planning stage

Reference 18

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This paper cites Quantile regression.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Quantile regression

Reference 19

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This paper cites Bayesian lstm for indoor temperature modeling.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Bayesian lstm for indoor temperature modeling

Reference 20

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This paper cites Gaussian process modeling for measurement and verification of building energy savings.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Gaussian process modeling for measurement and verification of building energy savings

Reference 21

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This paper cites Building simulation: Ten challenges.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Building simulation: Ten challenges

Reference 22

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Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Unresolved cited work

Reference 23

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This paper cites Buildings - Energy System.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Buildings - Energy System

Reference 24

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This paper cites quantile-forest: A python package for quantile regression forests.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling quantile-forest: A python package for quantile regression forests

Reference 25

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This paper cites Benchmarking HVAC controller performance with a digital twin, in: Energy Proceedings, pp.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Benchmarking HVAC controller performance with a digital twin, in: Energy Proceedings, pp

Reference 26

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This paper cites Combining Physics-based and Data-driven Modeling for Building Energy Systems.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Combining Physics-based and Data-driven Modeling for Building Energy Systems

Reference 27

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This paper cites Distribution-Free Predictive Inference for Regression.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Distribution-Free Predictive Inference for Regression

Reference 28

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This paper cites Energy-saving potential benchmarking method of office buildings based on probabilistic forecast.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Energy-saving potential benchmarking method of office buildings based on probabilistic forecast

Reference 29

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This paper cites A review of physics-informed machine learning for building energy modeling.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling A review of physics-informed machine learning for building energy modeling

Reference 30

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This paper cites AhybridapproachtothermalbuildingmodellingusingacombinationofGaussianprocessesandgrey-box models.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling AhybridapproachtothermalbuildingmodellingusingacombinationofGaussianprocessesandgrey-box models

Reference 31

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This paper cites Quantile regression forests.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Quantile regression forests

Reference 32

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This paper cites Change-point multivariable quantile regression to explore effect of weather variables on building energy consumption and estimate base temperature range.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Change-point multivariable quantile regression to explore effect of weather variables on building energy consumption and estimate base temperature range

Reference 33

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Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Unresolved cited work

Reference 34

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This paper cites Weighted aggregated ensemble model for energy demand management of buildings.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Weighted aggregated ensemble model for energy demand management of buildings

Reference 35

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This paper cites Pytorch: An imperative style, high-performance deep learning library, in: Advances in Neural Information Processing Systems 32.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Pytorch: An imperative style, high-performance deep learning library, in: Advances in Neural Information Processing Systems 32

Reference 36

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This paper cites Demand response for residential building heating: Effective Monte Carlo Tree Search control based on physics-informed neural networks.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Demand response for residential building heating: Effective Monte Carlo Tree Search control based on physics-informed neural networks

Reference 37

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This paper cites NEST – una plataforma para acelerar la innovación en edificios.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling NEST – una plataforma para acelerar la innovación en edificios

Reference 38

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This paper cites Conformalized quantile regression.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Conformalized quantile regression

Reference 39

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This paper cites Building consumption anomaly detection: A comparative study of two probabilistic approaches.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Building consumption anomaly detection: A comparative study of two probabilistic approaches

Reference 40

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This paper cites Department of Energy, 2017.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Department of Energy, 2017

Reference 41

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Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Algorithmic Learning in a Random World

Reference 42

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Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Unresolved cited work

Reference 43

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This paper cites Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models

Reference 44

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This paper cites A decision-theoretic approach to interval estimation.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling A decision-theoretic approach to interval estimation

Reference 45

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This paper cites Conformal prediction interval for dynamic time-series, in: Meila, M., Zhang, T.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Conformal prediction interval for dynamic time-series, in: Meila, M., Zhang, T

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:50:19.968262Z

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Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Unresolved cited work

Reference 47

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This paper cites Stateoftheartreviewonmodelpredictivecontrol(MPC)inHeatingVentilationandAir-conditioning(HVAC) field.

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Stateoftheartreviewonmodelpredictivecontrol(MPC)inHeatingVentilationandAir-conditioning(HVAC) field

Reference 48

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Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling Unresolved cited work

Reference 1522

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