The Power of Diversity: Data-Driven Robust Predictive Control for Energy Efficient Buildings and Districts
Georgios Darivianakis, Angelos Georghiou, Roy S. Smith, John, Lygeros

TL;DR
This paper presents a data-driven robust optimization approach for cooperative energy management in buildings, leveraging historical data to improve energy savings and reduce constraint violations in large-scale systems.
Contribution
It introduces a novel multistage stochastic optimization framework that uses distribution families from historical data, scalable to large systems, for energy-efficient building management.
Findings
Outperforms existing methods in energy cost savings
Reduces constraint violations in energy management
Achieves significant energy gains in heterogeneous building collections
Abstract
The cooperative energy management of aggregated buildings has recently received a great deal of interest due to substantial potential energy savings. These gains are mainly obtained in two ways: (i) Exploiting the load shifting capabilities of the cooperative buildings; (ii) Utilizing the expensive but energy efficient equipment that is commonly shared by the building community (e.g., heat pumps, batteries and photovoltaics). Several deterministic and stochastic control schemes that strive to realize these savings, have been proposed in the literature. A common difficulty with all these methods is integrating knowledge about the disturbances affecting the system. In this context, the underlying disturbance distributions are often poorly characterized based on historical data. In this paper, we address this issue by exploiting the historical data to construct families of distributions…
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