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Data-Driven Predictive Control for Commercial Buildings with Multiple Energy Flexibility Sources
Date Issued
2020-10-01
Date Available
2021-01-19T11:44:56Z
Abstract
Data-Driven Predictive Control, representing the building as a cyber-physical system, shows promising potential in harnessing energy flexibility for demand side management, where the efforts in developing a physics-based model can be significant. Here, predictive control using random forests is applied in a case study closed-loop simulation of a large office building with multiple energy flexibility sources, thereby testing the suitability of the technique for such buildings. Further, consideration is given to the feature selection and feature engineering process. The results show that the data-driven predictive control, under a dynamic grid signal, is capable of minimising energy consumption or energy cost.
Sponsorship
Science Foundation Ireland
Other Sponsorship
ESIPP UCD
Type of Material
Conference Publication
Publisher
ASHRAE
Copyright (Published Version)
2020 ASHRAE
Language
English
Status of Item
Peer reviewed
Conference Details
The 2020 Building Performance Analysis Simbuild Virtual Conference, Online, 29 September – 1 October 2020
This item is made available under a Creative Commons License
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