@article {31362,
	title = {Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems},
	journal = {Applied Energy},
	volume = {236},
	year = {2019},
	month = {02/2019},
	pages = {410 - 425},
	abstract = {<p>Model predictive control (MPC) for buildings is attracting significant attention in research and industry due to its potential to address a number of challenges facing the building industry, including energy cost reduction, grid integration, and occupant connectivity. However, the strategy has not yet been implemented at any scale, largely due to the significant effort required to configure and calibrate the model used in the MPC controller. While many studies have focused on methods to expedite model configuration and improve model accuracy, few have studied the impact a wide range of factors have on the accuracy of the resulting model. In addition, few have continued on to analyze these factors{\textquoteright} impact on MPC controller performance in terms of final operating costs. Therefore, this study first identifies the practical factors affecting model setup, specifically focusing on the thermal envelope. The seven that are identified are building design, model structure, model order, data set, data quality, identification algorithm and initial guesses, and software tool-chain. Then, through a large number of trials, it analyzes each factor{\textquoteright}s influence on model accuracy, focusing on grey-box models for a single zone building envelope. Finally, this study implements a subset of the models identified with these factor variations in heating, ventilating, and air conditioning MPC controllers, and tests them in simulation of a representative case that aims to optimally cool a single-zone building with time-varying electricity prices. It is found that a difference of up to 20\% in cooling cost for the cases studied can occur between the best performing model and the worst performing model. The primary factors attributing to this were model structure and initial parameter guesses during parameter estimation of the model.</p>},
	keywords = {building simulation, hvac, Model predictive control, System identification},
	issn = {03062619},
	doi = {10.1016/j.apenergy.2018.11.093},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0306261918318099https://api.elsevier.com/content/article/PII:S0306261918318099?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0306261918318099?httpAccept=text/plain},
	author = {David Blum and K. Arendt and Lisa Rivalin and Mary Ann Piette and Michael Wetter and C.T. Veje}
}
@conference {31980,
	title = {Prototyping the BOPTEST Framework for Simulation-Based Testing of Advanced Control Strategies in Buildings},
	booktitle = {IBPSA Building Simulation 2019},
	year = {2019},
	address = {Rome, Italy},
	abstract = {<p>Advanced control strategies are becoming increasingly necessary in buildings in order to meet and balance requirements for energy efficiency, demand flexibility, and occupant comfort. Additional development and widespread adoption of emerging control strategies, however, ultimately require low implementation costs to reduce payback period and verified performance to gain control vendor, building owner, and operator trust. This is difficult in an already first-cost driven and risk-averse industry. Recent innovations in building simulation can significantly aid in meeting these requirements and spurring innovation at early stages of development by evaluating performance, comparing state-of-the-art to new strategies, providing installation experience, and testing controller implementations. This paper presents the development of a simulation framework consisting of test cases and software platform for the testing of advanced control strategies (BOPTEST - Building Optimization Performance Test). The objectives and requirements of the framework, components of a test case, and proposed software platform architecture are described, and the framework is demonstrated with a prototype implementation and example test case.</p>},
	keywords = {benchmarking, building simulation, Model predictive control, software development},
	author = {David Blum and Filip Jorissen and Sen Huang and Yan Chen and Javier Arroyo and Kyle Benne and Yanfei Li and Valentin Gavan and Lisa Rivalin and Lieve Helsen and Draguna Vrabie and Michael Wetter and Marina Sofos}
}
