A Modelling Workflow for Predictive Control in Residential Buildings

Book chapter


O’Dwyer, E., Atam, E., Falugi, P., Kerrigan, E. C., Zagorowska, M. A. and Shah, N. 2022. A Modelling Workflow for Predictive Control in Residential Buildings. in: Vahidinasab, V. and Mohammadi-Ivatloo, B. (ed.) Active Building Energy Systems: Operation and Control Springer, Cham. pp. 99-128
AuthorsO’Dwyer, E., Atam, E., Falugi, P., Kerrigan, E. C., Zagorowska, M. A. and Shah, N.
EditorsVahidinasab, V. and Mohammadi-Ivatloo, B.
Abstract

Despite a large body of research, the widespread application of Model Predictive Control (MPC) to residential buildings has yet to be realised. The modelling challenge is often cited as a significant obstacle. This chapter establishes a systematic workflow, from detailed simulation model development to control-oriented model generation to act as a guide for practitioners in the residential sector. The workflow begins with physics-based modelling methods for analysis and evaluation. Following this, model-based and data-driven techniques for developing low-complexity, control-oriented models are outlined. Through sections detailing these different stages, a case study is constructed, concluding with a final section in which MPC strategies based on the proposed methods are evaluated, with a price-aware formulation producing a reduction in operational space-heating cost of 11%. The combination of simulation model development, control design and analysis in a single workflow can encourage a more rapid uptake of MPC in the sector.

Book titleActive Building Energy Systems: Operation and Control
Page range99-128
Year2022
PublisherSpringer, Cham
Publication dates
Online03 Aug 2021
Print07 May 2022
Publication process dates
Deposited04 Jul 2023
Edition1st
SeriesGreen Energy and Technology
ISBN9783030797416
9783030797423
ISSN1865-3529
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-030-79742-3_5
Web address (URL)https://link.springer.com/book/10.1007/978-3-030-79742-3
Permalink -

https://repository.uel.ac.uk/item/8w3w0

  • 21
    total views
  • 1
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as

Related outputs

Automatic scenario generation for efficient solution of robust optimal control problems
Zagorowska, M., Falugi, P., O'Dwyer, E. and Kerrigan E. C. 2024. Automatic scenario generation for efficient solution of robust optimal control problems. International Journal of Robust and Nonlinear Control. 34 (2), pp. 1370-1396. https://doi.org/10.1002/rnc.7038
Automatic Scenario Generation for Robust Optimal Control Problems
Zagorowska, M., Falugi, P., O'Dwyer, E. and Kerrigan, E. C. 2023. Automatic Scenario Generation for Robust Optimal Control Problems. IFAC 2023: 22nd World Congress of the International Federation of Automatic Control. Yokohama, Japan 09 - 14 Jul 2023 Elsevier for the International Federation of Automatic Control. https://doi.org/10.1016/j.ifacol.2023.10.1743
Automating the data-driven predictive control design process for building thermal management
Falugi, P., O'Dwyer, E., Shah, N. and Kerrigan, E. C. 2022. Automating the data-driven predictive control design process for building thermal management. ECOS 2022 35th International Conference. Copenhagen, Denmark 03 - 07 Jul 2022 Danmarks Tekniske Universitet (DTU). https://doi.org/10.11581/dtu.00000267
MPC and Optimal Design of Residential Buildings with Seasonal Storage: A Case Study
Falugi, P., O’Dwyer, E., Zagorowska, M. A., Atam, E., Kerrigan, E. C., Strbac, G. and Shah, N. 2022. MPC and Optimal Design of Residential Buildings with Seasonal Storage: A Case Study. in: Vahidinasab, V. and Mohammadi-Ivatloo, B. (ed.) Active Building Energy Systems: Operation and Control Springer, Cham. pp. 129-160
Fast and accurate method for computing non-smooth solutions to constrained control problems
Nita, L., Vila, E. M. G., Zagorowska, M. A., Kerrigan, E. C., Nie, Y., McInerney, I. and Falugi, P. 2022. Fast and accurate method for computing non-smooth solutions to constrained control problems. European Control Conference (ECC) 2022. London, UK 12 - 15 Jul 2022 IEEE. https://doi.org/10.23919/ECC55457.2022.9838569
Data-Driven Predictive Control With Improved Performance Using Segmented Trajectories
O’Dwyer, E., Kerrigan, E. C., Falugi, P., Zagorowska, M. and Shah, N. 2022. Data-Driven Predictive Control With Improved Performance Using Segmented Trajectories. IEEE Transactions on Control Systems Technology . 31 (3), pp. 1355 - 1365. https://doi.org/10.1109/TCST.2022.3224330
Predictive control co-design for enhancing flexibility in residential housing with battery degradation
Falugi, P., O’Dwyer, E., Kerrigan, E. C., Atam, E., Zagorowska, M. A., Strbac, G. and Shah, N. 2021. Predictive control co-design for enhancing flexibility in residential housing with battery degradation. 7th IFAC Conference on Nonlinear Model Predictive Control NMPC 2021. Bratislava, Slovakia 11 - 14 Jul 2021 Elsevier for the International Federation of Automatic Control. https://doi.org/10.1016/j.ifacol.2021.08.517
Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty
Falugi, P., Giannelos S, Jain A., Borozan S., Moreira A., Bhakar R., Mathur J. and Strbac G. 2021. Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty. Energies. 14 (22), p. 7813. https://doi.org/10.3390/en14227813
Robust and automatic data cleansing method for short-term load forecasting of distribution feeders
Huyghues-Beaufond, N., Tindemans, S., Falugi, P., Sun, M. and Strbac, G. 2020. Robust and automatic data cleansing method for short-term load forecasting of distribution feeders. Applied Energy. 261 (Art. 114405). https://doi.org/10.1016/j.apenergy.2019.114405