Optimal adaptive predictive control and fault detection of residential building heating systems

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]

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Model Predictive Control for the Operation of Building Cooling Systems

Ma

¹

,

Borrelli

²

,

Hencey

³

et al. 2012

IEEE Trans. Contr. Syst. Technol.

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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]

show abstract

Optimal adaptive predictive control and fault detection of residential building heating systems

Cited by 3 publications

References 5 publications

Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation

Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation

Model predictive control for the operation of building cooling systems

Model Predictive Control for the Operation of Building Cooling Systems

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