@manual{ashrae2014guideline,
  title        = {ASHRAE Guideline 14-2014: Measurement of Energy, Demand, and Water Savings},
  author       = {{ASHRAE}},
  year         = {2014},
  organization = {American Society of Heating, Refrigerating and Air-Conditioning Engineers}
}
@manual{ipmvp2012,
  title        = {International Performance Measurement and Verification Protocol: Concepts and Options},
  author       = {{EVO}},
  year         = {2012},
  organization = {Efficiency Valuation Organization}
}
@article{crawley2001energyplus,
  title={EnergyPlus: creating a new-generation building energy simulation program},
  author={Crawley, Drury B and others},
  journal={Energy and Buildings},
  volume={33},
  number={4},
  pages={319--331},
  year={2001}
}
@manual{klein2017trnsys,
  title={TRNSYS 18: A Transient System Simulation Program},
  author={Klein, Sanford A and Beckman, William A and Mitchell, John W and others},
  year={2017},
  organization={Solar Energy Laboratory, UW-Madison}
}
@inproceedings{wang2015modelica,
  title={The Modelica Buildings Library},
  author={Wang, D and Wetter, M and Zuo, W and Nouidui, T},
  booktitle={Proceedings of BS2015},
  year={2015}
}
@article{drgona2020all,
  title={All you need to know about model predictive control for buildings},
  author={Drgo{\v{n}}a, J and Arroyo, J and Cupeiro Figueroa, I and others},
  journal={Annual Reviews in Control},
  volume={50},
  pages={190--232},
  year={2020}
}
@article{heinen2022flexibility,
  title={Flexibility in buildings: A review of demand response and control},
  author={Heinen, S and et al.},
  journal={Renewable and Sustainable Energy Reviews},
  volume={153},
  pages={111763},
  year={2022}
}
@article{ahmad2017review,
  title={A comprehensive review on energy consumption forecasting in buildings},
  author={Ahmad, Tanveer and Chen, Huan},
  journal={Renewable and Sustainable Energy Reviews},
  volume={72},
  pages={1103--1122},
  year={2017}
}
@article{li2021review,
  title={Machine learning for building energy prediction and control: A review},
  author={Li, W and others},
  journal={Energy and Buildings},
  volume={224},
  pages={110132},
  year={2021}
}
@article{smarra2018data,
  title={Data-driven model predictive control using random forests for building energy management},
  author={Smarra, F and Jain, A and others},
  journal={Applied Energy},
  volume={226},
  pages={1252--1272},
  year={2018}
}
@misc{resstock2017,
  title={ResStock: High-Resolution Modeling of the U.S. Housing Stock},
  author={Wilson, E and Metzger, C and Horowitz, S and Hendron, R},
  year={2017},
  howpublished={NREL Technical Report},
  note={NREL/TP-5500-68570}
}
@misc{wolf2014inspire,
  title={D2.1e--Catalogue of energy retrofit measures for non-residential buildings},
  author={Wolf, S and et al.},
  year={2014},
  howpublished={iNSPiRe FP7 Project Report},
  note={European Commission FP7}
}
@inproceedings{chen2016xgboost,
  title={XGBoost: A scalable tree boosting system},
  author={Chen, Tianqi and Guestrin, Carlos},
  booktitle={Proceedings of KDD},
  pages={785--794},
  year={2016}
}
@inproceedings{ke2017lightgbm,
  title={LightGBM: A highly efficient gradient boosting decision tree},
  author={Ke, G and et al.},
  booktitle={Proceedings of NeurIPS},
  year={2017}
}
@inproceedings{lundberg2017shap,
  title={A unified approach to interpreting model predictions},
  author={Lundberg, Scott and Lee, Su-In},
  booktitle={NeurIPS},
  year={2017}
}
@article{angelopoulos2021gentle,
  title={A gentle introduction to conformal prediction},
  author={Angelopoulos, Anastasios N and Bates, Stephen},
  journal={arXiv preprint arXiv:2107.07511},
  year={2021}
}
@article{hong2020transfer,
  title={Transfer learning for building energy prediction: A review},
  author={Hong, Tianzhen and et al.},
  journal={Energy and Buildings},
  volume={203},
  pages={109516},
  year={2020}
}
@article{mahnke2022transfer,
  title={Transfer learning for cross-building energy modeling with small data},
  author={Mahnke, Robert and others},
  journal={Applied Energy},
  volume={307},
  pages={118244},
  year={2022}
}
@inproceedings{li2022domain,
  title={Domain adaptation for building energy prediction under covariate shift},
  author={Li, Peng and others},
  booktitle={IEEE PES General Meeting},
  year={2022}
}
