Abstract-A preliminary study on the application of a modelbased predictive control (MPC) of thermal energy storage in building cooling systems is presented. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. Typically the chillers are operated each night to recharge the storage tank in order to meet the buildings demand on the following day. A MPC for the chillers operation is designed in order to optimally store the thermal energy in the tank by using predictive knowledge of building loads and weather conditions. This paper addresses real-time implementation and feasibility issues of the MPC scheme by using a (1) simplified hybrid model of the system, (2) periodic robust invariant sets as terminal constraints and (3) a moving window blocking strategy.

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Abstract-A model-based predictive control (MPC) is designed for optimal thermal energy storage in building cooling systems. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. Typically the chillers are operated at night to recharge the storage tank in order to meet the building demands on the following day. In this paper, we build on our previous work, improve the building load model, and present experimental results. The experiments show that MPC can achieve reduction in the central plant electricity cost and improvement of its efficiency. I. INTRODUCTIONThe building sector consumes about 40% of the energy used in the United States and is responsible for nearly 40% of greenhouse gas emissions [12]. It is therefore economically, socially and environmentally significant to reduce the energy consumption of buildings.Reductions of 70% in energy use in buildings are required to achieve the goals for the building sector set by organizations such as the California Public Utilities Commission. Achieving this goal requires the development of highly efficient heating and cooling systems, which are more challenging to control than conventional systems [8], [7], [2].This work focuses on the modeling and the control of the central plant (thermal energy generation and storage system) at the University of California at Merced in USA. The campus has a significantly enhanced level of instrumentation in order to support the development and demonstration of energy-efficient technologies and practices. It consists of a chiller plant (three chillers redundantly configured as two in series, one backup in parallel), an array of cooling towers, a 7000 m 3 chilled water tank, a primary distribution system and secondary distribution loops serving each building of the campus. The two series chillers are operated each night to charge the storage tank to meet campus cooling demand the following day. Although the storage tank enables load shifting to off-peak hours, the lack of an optimized operation results in conservatively over-charging the tank.A simplified model of the central plant and a MPC strategy has been presented and discussed in [10]. The work in [10]

a b s t r a c tThis paper presents an occupancy-predicting control algorithm for heating, ventilation, and air conditioning (HVAC) systems in buildings. It incorporates the building's thermal properties, local weather predictions, and a self-tuning stochastic occupancy model to reduce energy consumption while maintaining occupant comfort. Contrasting with existing approaches, the occupancy model requires no manual training and adapts to changes in occupancy patterns during operation. A prediction-weighted cost function provides conditioning of thermal zones before occupancy begins and reduces system output before occupancy ends. Simulation results with real-world occupancy data demonstrate the algorithm's effectiveness.