Adaptive Data-Driven Prediction in a Building Control Hierarchy: A Case Study of Demand Response in Switzerland
CoRR(2023)
摘要
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
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.
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关键词
building control,predictive control,demand response,data-driven
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