@conference {31839, title = {Comparison of MPC Formulations for Building Control under Commercial Time-of-Use Tariffs}, booktitle = {IEEE PowerTech Milan 2019}, year = {2019}, month = {2019}, abstract = {

Most medium and large commercial buildings in\ the U.S. are subject to complex electricity tariffs that combine\ both Time-of-Use (TOU) energy and demand charges. This study\ analyses the performances of different economic Model Predictive\ Control (MPC) formulations, from the standpoints of monthly bill\ reduction, load shifting, and peak demand reduction. Simulations\ are performed on many simplified commercial building models,\ with multiple TOU demand charges, and under various summer\ conditions. Results show that compared to energy-only MPC, the\ traditional method for dealing with demand charges significantly
reduces peak demand and owner bill, however, highlight a lack\ of load shifting capability. A proposed incremental approach
is presented, which better balances the bill components in the\ objective function. In the case study presented, this method
can improve monthly bill savings and increase load shifting\ during demand response events, while keeping a similarly low
peak demand, compared to traditional MPC methods taking into\ account demand charges.

}, keywords = {commercial building, demand charge, Model predictive control (MPC), peak demand, time-of-use tarrif}, author = {Olivier Van Cutsem and Maher Kayal and David Blum and Marco Pritoni} } @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 = {

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.

}, 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 = {

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.

}, 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} } @conference {31355, title = {When Data Analytics Meet Site Operation: Benefits and Challenges}, booktitle = {2018 ACEEE Summer Study on Energy Efficiency in Buildings}, year = {2018}, month = {08/2018}, abstract = {

Demand for using data analytics for energy management in buildings is rising. Such analytics are required for advanced measurement and verification, commissioning, automated fault-detection and diagnosis, and optimal control. While novel analytics algorithms continue to be developed, bottlenecks and challenges arise when deploying them for demonstration, for a number of reasons that do not necessarily have to do with the algorithms themselves. It is important for developers of new technologies to be aware of the challenges and potential solutions during demonstration. Therefore, this paper describes a recent deployment of an automated, physical model-based, FDD and optimal control tool, highlighting its design and as-operated benefits that the tool provides. Furthermore, the paper presents challenges faced during deployment and testing along with solutions used to overcome these challenges. The challenges have been grouped into four categories: Data Management, Physical Model Development and Integration, Software Development and Deployment, and Operator Use. The paper concludes by discussing how challenges with this project generalize to common cases, how they could compare to other projects in their severity, and how they may be addressed.

}, author = {David Blum and Guanjing Lin and Michael Spears and Janie Page and Jessica Granderson} } @conference {31830, title = {MPCPy: An Open-Source Software Platform for Model Predictive Control in Buildings}, booktitle = {Proceedings of the 15th IBPSA Conference: Building Simulation 2017}, year = {2017}, month = {08/2017}, address = {San Francisco}, abstract = {

Within the last decade, needs for building control systems that reduce cost, energy, or peak demand, and that facilitate building-grid integration, district-energy system optimization, and occupant interaction, while maintaining thermal comfort and indoor air quality, have come about.\  Current PID and schedule-based control systems are not capable of fulfilling these needs, while Model Predictive Control (MPC) could.\  Despite the critical role MPC-enabled buildings can play in future energy infrastructures, widespread adoption of MPC within the building industry has yet to occur.\  To address barriers associated with system setup and configuration, this paper introduces an open-source software platform that emphasizes use of self-tuning adaptive models, usability by non-experts of MPC, and a flexible architecture that enables application across projects.

}, keywords = {Model predictive control (MPC)}, url = {http://www.ibpsa.org/proceedings/BS2017/BS2017_351.pdf}, author = {David Blum and Michael Wetter} }