Abstract
Load management actions in large buildings are pre-programmed by field engineers/users in the form of if-then-else rules for the set point of the thermostat. This fixed set of actions prevents smart zoning, i.e. to dynamically regulate the set points in every room at different levels according to geometry, orientation and interaction among rooms caused by occupancy patterns. In this work we frame the problem of load management with smart zoning into a multiple-mode feedback-based optimal control problem: multiple-mode refers to embedding multiple behaviors (triggered by building-occupant dynamic interaction) into the optimization problem; feedback-based refers to adopting a Hamilton-Jacobi-Bellman framework, with closed-loop control strategies using information stemming from building and weather states. The framework is solved by parameterizing the candidate control strategies and by searching for the optimal strategy in an adaptive self-tuning way. To demonstrate the proposed approach, we employ an EnergyPlus model of an actual office building in Crete, Greece. Extensive tests show that the proposed solution is able to provide, dynamically and autonomously, dedicated set points levels in every room in such a way to optimize the whole building performance (exploitation of renewable energy sources with improved thermal comfort). As compared to pre-programmed (non-optimal) strategies, we show that smart zoning makes it is possible to save more than 15% energy consumption, with 25% increased thermal comfort. As compared to optimized strategies in which smart zoning is not implemented, smart zoning leads to additional 4% reduced energy and 8% improved comfort, demonstrating improved occupant-building interaction. Such improvements are motivated by the fact that the approach exploits the building dynamics as learned from feedback data. Moreover, the closed-loop feature of the approach makes it robust to variable weather conditions and occupancy schedules.
Highlights
The future will see more and more developments in the smart buildings and smart grids areas [1]: while smart buildings should implement demand management programs [2], smart grids should implement demand response programs that can modify normal consumption patterns in buildings depending on the state of the grid [3]
As compared to pre-programmed strategies, we show that smart zoning makes it is possible to save more than 15% energy consumption, with 25% increased thermal comfort
As compared to optimized strategies in which smart zoning is not implemented, smart zoning leads to additional 4% reduced energy and 8% improved comfort, demonstrating improved occupant-building interaction
Summary
The future will see more and more developments in the smart buildings and smart grids areas [1]: while smart buildings should implement demand management programs (sometimes referred to as load management programs) [2], smart grids should implement demand response programs that can modify normal consumption patterns in buildings depending on the state of the grid [3]. Automated demand management presents several challenges, one of the main ones being enhancing energy efficiency in thermostatically controlled HVAC loads via smart zoning [5]. While being appealing and in many case effective, such solutions work for small homes, and demand management actions often consist of a fixed (nondynamic) set of rule-based options. This fixed set of options often neglects the building dynamics and the dynamic occupant-building interaction: in order to keep consistent performance, the HVAC set points should be continuously adjusted depending on variable weather conditions (which will affect the availability of renewable energy sources) or depending on user activity (which will affect occupancy patterns). Some open problems in these areas are discussed, from which the motivations for this work arise
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