A Model Predictive Control algorithm was developed for the UC Merced campus chilled water plant. Model predictive control (MPC) is an advanced control technology that has proven successful in the chemical process industry and other industries [1][2][3]. The main goal of the research was to demonstrate the practical and commercial viability of MPC for optimization of building energy systems. The control algorithms were developed and implemented in MATLAB, allowing for rapid development, performance, and robustness assessment.The UC Merced chilled water plant includes three water-cooled chillers and a two million gallon chilled water storage tank. The tank is charged during the night to minimize on-peak electricity consumption and take advantage of the lower ambient wet bulb temperature. The control algorithms determined the optimal chilled water plant operation including chilled water supply (CHWS) temperature set-point, condenser water supply (CWS) temperature set-point and the charging start and stop times to minimize a cost function that includes energy consumption and peak electrical demand over a 3-day prediction horizon.A detailed model of the chilled water plant and simplified models of the buildings served by the plant were developed using the equation-based modeling language Modelica. Steady state models of the chillers, cooling towers and pumps were developed, based on manufacturers' performance data, and calibrated using measured data collected and archived by the control system. A detailed dynamic model of the chilled water storage tank was also developed and calibrated. Simple, semi-empirical models were developed to predict the temperature and flow rate of the chilled water returning to the plant from the buildings. These models were then combined and simplified for use in a model predictive control algorithm that determines the optimal chiller start and stop times and set-points for the condenser water temperature and the chilled water supply temperature. The report describes the development and testing of the algorithm and evaluates the resulting performance, concluding with a discussion of next steps in further research.The experimental results show a small improvement in COP over the baseline policy but it is difficult to draw any strong conclusions about the energy savings potential for MPC with this system only four days of suitable experimental data were obtained once correct operation of the MPC system had been achieved. These data show an improvement in COP of 3.1% ±2.2% relative to a baseline established immediately prior to the period when the MPC was run in its final form. This baseline includes control policy improvements that the plant operators learned by observing the earlier implementations of MPC, including increasing the temperature of the water supplied to the chiller condensers from the cooling towers. The process of data collection and model development, necessary for any MPC project, resulted in the team uncovering various problems with the chilled water system. Although it is...

The control performance of an air-conditioning system is assessed using a qualitative method of evaluation. Fuzzy logic is used to relate performance criteria expressed in the form of IF-THEN rules to quantitative measures of energy consumption, discomfort, and maintenance costs. Test data were generated using an emulator consisting of a real-time simulation of the building shell and HVAC plant, together with a hardware interface that connects the simulation to commercial control equipment. Two case studies are presented. In the first, the effect of changing the strategy used to determine the zone temperature set-points is evaluated using 'expert rules', generated by a hypothetical facilities manager. In the second case study, the effect of varying the tuning parameters of the control system is evaluated using two sets of rules assumed to represent the differing perspectives of a facilities manager and a control engineer.

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Calculation of design cooling loads is of critical concern to designers of HVAC systems. The work reported here has been carried out under a joint ASHRAE-CIBSE research project to compare design cooling calculation methods. Peak cooling loads predicted by the ASHRAE heat balance method are compared with those predicted by a number of implementations of the admittance method using different window models. The results presented show the general trends in overprediction or underprediction of peak load. Particular attention is given to different window modelling practices. The performance of the methods is explained in terms of some of the underlying assumptions in the window models, and by reference to specific inter-model comparisons.

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