Browsing by Subject "energy consumption"
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Item Methodology to Analyze the Sensitivity of Building Energy Consumption to HVAC System Sensor Error(2012-02-14) Ma, LiangThis thesis proposes a methodology for determining sensitivity of building energy consumption of HVAC systems to sensor error. It is based on a series of simulations of a generic building, the model for which is based on several typical input parameters. There are a total of eight scenarios considered in this simulation. The simulation tool was developed based on Excel. The control parameters examined include room temperature, cold deck temperature, hot deck temperature, pump pressure, and fan pressure. All of the parameters considered are varied in order to analyze the sensitivity of building energy consumption to their variation. In this tool, different operation schedules for equipment, occupancy, and lighting are considered. By changing each control parameter, the sensitivity of energy use to sensor error is simulated, a regression model is generated, and the energy consumption change is expressed as a function of sensor error and outside air percentage. Two applications of this methodology are presented in this thesis. One is a SDVAV system and the other is a DDVAV system. The outside air percentage changes the trend of the sensor error curve. After the sensitivity study is discussed, some recommendations regarding the calibration intervals of the sensors are given.Item Minimizing Energy Consumption in a Water Distribution System: A Systems Modeling Approach(2011-08-08) Johnston, JohnIn a water distribution system from groundwater supply, the bulk of energy consumption is expended at pump stations. These pumps pressurize the water and transport it from the aquifer to the distribution system and to elevated storage tanks. Each pump in the system has a range of possible operating conditions with varying flow rates, hydraulic head imparted, and hydraulic efficiencies. In this research, the water distribution system of a mid-sized city in a subtropical climate is modeled and optimized in order to minimize the energy usage of its fourteen pumps. A simplified model of the pipes, pumps, and storage tanks is designed using freely-available EPANET hydraulic modeling software. Physical and operational parameters of this model are calibrated against five weeks of observed data using a genetic algorithm to predict storage tank volume given a forecasted system demand. Uncertainty analysis on the calibrated parameters is performed to assess model sensitivity. Finally, the pumping schedule for the system's fourteen pumps is optimized using a genetic algorithm in order to minimize total energy use across a 24-hour period.