Adaptive Data-Driven Prediction in a Building Control Hierarchy: A Case Study of Demand Response in Switzerland
Abstract: By providing various services, such as Demand Response (DR), buildings can play a crucial role in the energy market due to their significant energy consumption. However, effectively commissioning buildings for such desired functionalities requires significant expert knowledge and design effort, considering the variations in building dynamics and intended use. In this study, we introduce an adaptive data-driven prediction scheme based on Willems' Fundamental Lemma within the building control hierarchy. This scheme offers a versatile, flexible, and user-friendly interface for diverse prediction and control objectives. We provide an easy-to-use tuning process and an adaptive update pipeline for the scheme, both validated through extensive prediction tests. We evaluate the proposed scheme by coordinating a building and an energy storage system to provide Secondary Frequency Control (SFC) in a Swiss DR program. Specifically, we integrate the scheme into a three-layer hierarchical SFC control framework, and each layer of this hierarchy is designed to achieve distinct operational goals. Apart from its flexibility, our approach significantly improves cost efficiency, resulting in a 28.74% reduction in operating costs compared to a conventional control scheme, as demonstrated by a 52-day experiment in an actual building. Our findings emphasize the potential of the proposed scheme to reduce the commissioning costs of advanced building control strategies and to facilitate the adoption of new techniques in building control.
- U. N. E. Programme, 2022 global status report for buildings and construction (2022).
- doi:10.1016/j.rser.2017.09.102.
- doi:10.1016/j.rser.2015.01.057.
- doi:10.1016/j.buildenv.2013.03.010.
- doi:10.1016/j.buildenv.2013.11.016.
- doi:10.3390/en11030631.
- doi:10.1016/j.arcontrol.2020.09.001.
- doi:10.1016/j.jobe.2020.101692.
- doi:10.1016/j.rser.2020.110120.
- doi:10.1016/j.enbuild.2018.03.082.
- doi:10.1016/j.enbuild.2018.05.048.
- doi:10.1109/tcst.2015.2415411.
- doi:10.1016/j.conengprac.2023.105536.
- doi:10.1016/j.apenergy.2021.116653.
- doi:10.1016/j.apenergy.2018.08.045.
- doi:10.1016/j.apenergy.2017.06.040. URL https://doi.org/10.1016%2Fj.apenergy.2017.06.040
- doi:10.1016/j.adapen.2022.100099.
- doi:10.1016/j.buildenv.2016.05.034.
- doi:10.1016/j.rser.2014.05.056.
- doi:10.1016/j.rser.2021.110835.
- doi:10.1109/mcs.2016.2535913.
- doi:10.1016/j.enbuild.2022.112316.
- doi:10.1016/j.apenergy.2018.02.126.
- doi:10.1016/j.enbuild.2012.08.002.
- doi:10.1016/j.enbuild.2010.10.022.
- doi:10.1109/cdc51059.2022.9992445.
- doi:10.1016/j.apenergy.2021.118491.
- doi:10.1016/j.enbuild.2012.10.024.
- doi:10.1007/978-1-4613-0465-4_6.
- doi:10.1109/cdc.2004.1428856.
- doi:10.1109/tcst.2021.3088638.
- doi:10.1109/lcsys.2022.3184904.
- doi:10.1109/tcst.2023.3288636.
- doi:10.1016/j.epsr.2022.108311.
- doi:10.23919/ecc.2019.8795639.
- doi:10.1115/1.4054314.
- doi:10.1016/j.apenergy.2023.122101.
- doi:10.1109/tcst.2023.3259641.
- doi:10.1016/j.buildenv.2018.02.015.
- doi:10.1109/lcsys.2023.3282987.
- doi:10.1109/lcsys.2020.2986991.
- doi:10.1109/tcst.2022.3224330.
- doi:10.1109/tac.2022.3148374.
- doi:10.1109/tac.2021.3137788.
- doi:10.1109/ptc.2017.7980924.
- doi:10.1109/tsg.2016.2632239.
- doi:10.1109/ptc.2017.7981118.
- doi:10.1137/1.9781611976595.
- doi:10.1016/j.automatica.2005.08.023.
- doi:10.1109/tcst.2013.2272178.
- doi:10.1109/cacsd.2004.1393890.
- doi:10.1002/we.284.
- doi:10.3403/30133865.
- doi:10.1016/j.apenergy.2022.119806.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.
