LAPSE:2023.23733
Published Article
LAPSE:2023.23733
Performance Assessment of Data-Driven and Physical-Based Models to Predict Building Energy Demand in Model Predictive Controls
March 27, 2023
The implementation of model predictive controls (MPCs) in buildings represents an important opportunity to reduce energy consumption and to apply demand side management strategies. In order to be effective, the MPC should be provided with an accurate model that is able to forecast the actual building energy demand. To this aim, in this paper, a data-driven model realized with an artificial neural network is compared to a physical-based resistance−capacitance (RC) network in an operative MPC. The MPC was designed to minimize the total cost for the thermal demand requirements by unlocking the energy flexibility in the building envelope, on the basis of price signals. Although both models allow energy cost savings (about 16% compared to a standard set-point control), a deterioration in the prediction performance is observed when the models actually operate in the controller (the root mean square error, RMSE, for the air zone prediction is about 1 °C). However, a difference in the on-time control actions is noted when the two models are compared. With a maximum deviation of 0.5 °C from the indoor set-point temperature, the physical-based model shows better performance in following the system dynamics, while the value rises to 1.8 °C in presence of the data-driven model for the analyzed case study. This result is mainly related to difficulties in properly training data-driven models for applications involving energy flexibility exploitation.
Keywords
artificial neural network, data-driven model, energy flexibility, Model Predictive Control, physical building model
Suggested Citation
Mugnini A, Coccia G, Polonara F, Arteconi A. Performance Assessment of Data-Driven and Physical-Based Models to Predict Building Energy Demand in Model Predictive Controls. (2023). LAPSE:2023.23733
Author Affiliations
Mugnini A: Dipartimento di Ingegneria Industriale e Scienze Matematiche, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy
Coccia G: Dipartimento di Ingegneria Industriale e Scienze Matematiche, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy [ORCID]
Polonara F: Dipartimento di Ingegneria Industriale e Scienze Matematiche, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy; Consiglio Nazionale delle Ricerche, Istituto per le Tecnologie della Costruzione, Viale Lombardia 49, 20098 San [ORCID]
Arteconi A: Dipartimento di Ingegneria Industriale e Scienze Matematiche, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy; Department of Mechanical Engineering, KU Leuven, B-3000 Leuven, Belgium [ORCID]
Journal Name
Energies
Volume
13
Issue
12
Article Number
E3125
Year
2020
Publication Date
2020-06-16
Published Version
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en13123125, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2023.23733
This Record
External Link

doi:10.3390/en13123125
Publisher Version
Download
Files
[Download 1v1.pdf] (4.7 MB)
Mar 27, 2023
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
86
Version History
[v1] (Original Submission)
Mar 27, 2023
 
Verified by curator on
Mar 27, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.23733
 
Original Submitter
Auto Uploader for LAPSE
Links to Related Works
Directly Related to This Work
Publisher Version