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• W2609706670 abstract "This thesis demonstrates how artificial intelligence algorithms can be applied to the operation of heating ventilation, and air conditioning (HVAC) systems in commercial scale buildings. Two different levels of operation are considered: gaining stable, precise control of feedback loops and the minimization of energy consumption from the entire HVAC plant. The control algorithms are used to predict both process and plant outputs, and the predictions are used in the optimizations. These techniques are also compared with more conventional methods of control.Artificial neural networks (ANNs) are computer-based emulators of biological cognitive systems. They have the unusual ability to learn relationships between sets of information that would be difficult to quantify using conventional means. For the experiments discussed in this thesis, the networks were used to find a correlation between the inputs and outputs of a number of physical processes--in this sense the ANNs behave much like nonlinear regressions.A properly trained ANN can be used to predict the response of a feedback loop and provides the base for a predictive control algorithm. Such a controller is extremely effective and can ensure critically damped response over the effective domain of nonlinear processes. The ability of an ANN-based controller to maintain the desired setpoint is shown to be as good or better than that of a more conventional PID controller.Similarly, a network trained to predict the energy consumption of a plant can be used to find setpoints which will minimize plant power while still maintaining comfort conditions. An HVAC laboratory was used to simulate several building configurations. ANNs were then tested to explore their ability to predict the energy consumption of the plant-wide systems. Once a suitable model was found, the HVAC system was subjected to day-long tests representing several load levels. These tests were repeated using three different plant control algorithms, including one where the plant setpoints were modified by an ANN controller. All three were able to maintain comfort levels, but at varying average power levels. The neural network controller had the smallest average and also a lower peak value than one of the other methods. (Abstract shortened by UMI.)" @default.
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• W2609706670 date "1992-01-01" @default.
• W2609706670 modified "2023-09-26" @default.
• W2609706670 title "Artificial neural networks for use in building systems control and energy management" @default.
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