The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Field demonstration and implementation analysis of model predictive control in an office HVAC system
Open accessAbstract
Model Predictive Control (MPC) is a promising technique to address growing needs for heating, ventilation, and air-conditioning (HVAC) systems to operate more efficiently and with greater flexibility. However, due to a number of factors, including the required implementation expertise, lack of high quality data, and a risk-adverse industry, MPC has yet to gain widespread adoption. While many previous studies have shown the advantages of MPC, few analyzed the implementation effort and associated practical challenges. In addition, previous work has developed an open-source, Modelica-based tool-chain that automatically generates optimal control, parameter estimation, and state estimation problems aimed at facilitating MPC implementation. Therefore, this study demonstrates usage of this tool-chain to implement MPC in a real office building, discusses practical challenges of implementing MPC, and estimates the implementation effort associated with various tasks in order to inform the development of future workflows and serve as an initial benchmark for their impact on reducing implementation effort. This study finds that the implemented MPC saves approximately 40% of HVAC energy over the existing control during a two-month trial period and that tasks related to data collection and controller deployment activities can each require as much effort as model generation.
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SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430
All you need to know about model predictive control for buildings
Drgoňa, J., Arroyo, J., Cupeiro Figueroa, I., Blum, D., Arendt, K., Kim, D., Ollé, E. P., Oravec, J., Wetter, M., Vrabie, D. L., et al. (2020) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2020.09.001 · Cited by 1,020
Self-citation
Cited by
Field test of Model Predictive Control in residential buildings for utility cost savings
Wang, D., Chen, Y., Wang, W., Gao, C., & Wang, Z. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113026 · Cited by 84
Self-citationHu, Z., Gao, Y., Sun, L., Mae, M., & Imaizumi, T. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123652 · Cited by 52
A zoned group control of indoor temperature based on MPC for a space heating building
Wang, H., Bo, S., Zhu, C., Hua, P., Xie, Z., Xu, C., Wang, T., Li, X., Wang, H., Lahdelma, R., et al. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2023.117196 · Cited by 44
Chen, Y., Gao, J., Yang, J., Berardi, U., & Cui, G. (2023) · Applied Energy · doi:10.1016/j.apenergy.2022.120613 · Cited by 29
Shi, H., Wang, W., Peng, B., Jiang, X., Su, C., & Li, P. (2026) · Chemical Engineering Science · doi:10.1016/j.ces.2025.122412 · Cited by 2
Metadata
- Authors
- Blum, D., Wang, Z., Weyandt, C., Kim, D., Wetter, M., Hong, T., & Piette, M. A.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 318
- Pages
- 119104
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W4229061904
- Cited by
- 200
- References
- 2
- FWCI
- 16.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Blum, D., Wang, Z., Weyandt, C., Kim, D., Wetter, M., Hong, T., & Piette, M. A. (2022). Field demonstration and implementation analysis of model predictive control in an office HVAC system. Applied Energy. https://doi.org/10.1016/j.apenergy.2022.119104
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