Matching Items (7)

154129-Thumbnail Image.png

Pavement surfaces impact on local temperature and building cooling energy consumption

Description

Pavement surface temperature is calculated using a fundamental energy balance model developed previously. It can be studied using a one-dimensional mathematical model. The input to the model is changed, to

Pavement surface temperature is calculated using a fundamental energy balance model developed previously. It can be studied using a one-dimensional mathematical model. The input to the model is changed, to study the effect of different properties of pavement on its diurnal surface temperatures. It is observed that the pavement surface temperature has a microclimatic effect on the air temperature above it. A major increase in local air temperature is caused by heating of solid surfaces in that locality. A case study was done and correlations have been established to calculate the air temperature above a paved surface. Validation with in-situ pavement surface and air temperatures were made. Experimental measurement for the city of Phoenix shows the difference between the ambient air temperature of the city and the microclimatic air temperature above the pavement is approximately 10 degrees Fahrenheit. One mitigation strategy that has been explored is increasing the albedo of the paved surface. Although it will reduce the pavement surface temperature, leading to a reduction in air temperature close to the surface, the increased pavement albedo will also result in greater reflected solar radiation directed towards the building, thus increasing the building solar load. The first effect will imply a reduction in the building energy consumption, while the second effect will imply an increase in the building energy consumption. Simulation is done using the EnergyPlus tool, to find the microclimatic effect of pavement on the building energy performance. The results indicate the cooling energy savings of an office building for different types of pavements can be variable as much as 30%.

Contributors

Agent

Created

Date Created
  • 2015

154084-Thumbnail Image.png

Self-configuring and self-adaptive environment control systems for buildings

Description

Lighting systems and air-conditioning systems are two of the largest energy consuming end-uses in buildings. Lighting control in smart buildings and homes can be automated by having computer controlled lights

Lighting systems and air-conditioning systems are two of the largest energy consuming end-uses in buildings. Lighting control in smart buildings and homes can be automated by having computer controlled lights and window blinds along with illumination sensors that are distributed in the building, while temperature control can be automated by having computer controlled air-conditioning systems. However, programming actuators in a large-scale environment for buildings and homes can be time consuming and expensive. This dissertation presents an approach that algorithmically sets up the control system that can automate any building without requiring custom programming. This is achieved by imbibing the system self calibrating and self learning abilities.

For lighting control, the dissertation describes how the problem is non-deterministic polynomial-time hard(NP-Hard) but can be resolved by heuristics. The resulting system controls blinds to ensure uniform lighting and also adds artificial illumination to ensure light coverage remains adequate at all times of the day, while adjusting for weather and seasons. In the absence of daylight, the system resorts to artificial lighting.

For temperature control, the dissertation describes how the temperature control problem is modeled using convex quadratic programming. The impact of every air conditioner on each sensor at a particular time is learnt using a linear regression model. The resulting system controls air-conditioning equipments to ensure the maintenance of user comfort and low cost of energy consumptions. The system can be deployed in large scale environments. It can accept multiple target setpoints at a time, which improves the flexibility and efficiency of cooling systems requiring temperature control.

The methods proposed work as generic control algorithms and are not preprogrammed for a particular place or building. The feasibility, adaptivity and scalability features of the system have been validated through various actual and simulated experiments.

Contributors

Agent

Created

Date Created
  • 2015

154775-Thumbnail Image.png

Sustainability assessment framework for infrastructure: application to buildings / by Jonghoon Kim

Description

In the United States, buildings account for 20–40% of the total energy consumption based on their operation and maintenance, which consume nearly 80% of their energy during their lifecycle. In

In the United States, buildings account for 20–40% of the total energy consumption based on their operation and maintenance, which consume nearly 80% of their energy during their lifecycle. In order to reduce building energy consumption and related problems (i.e. global warming, air pollution, and energy shortages), numerous building technology programs, codes, and standards have been developed such as net-zero energy buildings, Leadership in Energy and Environmental Design (LEED), and the American Society of Heating, Refrigerating, and Air-Conditioning Engineers 90.1. However, these programs, codes, and standards are typically utilized before or during the design and construction phases. Subsequently, it is difficult to track whether buildings could still reduce energy consumption post construction. This dissertation fills the gap in knowledge of analytical methods for building energy analysis studies for LEED buildings. It also focuses on the use of green space for reducing atmospheric temperature, which contributes the most to building energy consumption. The three primary objectives of this research are to: 1) find the relationship between building energy consumption, outside atmospheric temperature, and LEED Energy and Atmosphere credits (OEP); 2) examine the use of different green space layouts for reducing the atmospheric temperature of high-rise buildings; and 3) use data mining techniques (i.e. clustering, isolation, and anomaly detection) to identify data anomalies in the energy data set and evaluate LEED Energy and Atmosphere credits based on building energy patterns. The results found that buildings with lower OEP used the highest amount of energy. LEED OEP scores tended to increase the energy saving potential of buildings, thereby reducing the need for renovation and maintenance. The results also revealed that the shade and evaporation effects of green spaces around buildings were more effective for lowering the daytime atmospheric temperature in the range of 2°C to 6.5°C. Additionally, abnormal energy consumption patterns were found in LEED buildings that used anomaly detection methodology analysis. Overall, LEED systems should be evaluated for energy performance to ensure that buildings continue to save energy after construction.

Contributors

Agent

Created

Date Created
  • 2016

155081-Thumbnail Image.png

Assessment of pattern of energy consumption with varying building parameters

Description

ABSTRACT

A large fraction of the total energy consumption in the world comes from heating and cooling of buildings. Improving the energy efficiency of buildings to reduce the needs of

ABSTRACT

A large fraction of the total energy consumption in the world comes from heating and cooling of buildings. Improving the energy efficiency of buildings to reduce the needs of seasonal heating and cooling is one of the major challenges in sustainable development. In general, the energy efficiency depends on the geometry and material of the buildings. To explore a framework for accurately assessing this dependence, detailed 3-D thermofluid simulations are performed by systematically sweeping the parameter space spanned by four parameters: the size of building, thickness and material of wall, and fractional size of window. The simulations incorporate realistic boundary conditions of diurnally-varying temperatures from observation, and the effect of fluid flow with explicit thermal convection inside the building. The outcome of the numerical simulations is synthesized into a simple map of an index of energy efficiency in the parameter space which can be used by stakeholders to quick look-up the energy efficiency of a proposed design of a building before its construction. Although this study only considers a special prototype of buildings, the framework developed in this work can potentially be used for a wide range of buildings and applications.

Contributors

Agent

Created

Date Created
  • 2016

152124-Thumbnail Image.png

Leveraging smart meter data through advanced analytics: applications to building energy efficiency

Description

The poor energy efficiency of buildings is a major barrier to alleviating the energy dilemma. Historically, monthly utility billing data was widely available and analytical methods for identifying building energy

The poor energy efficiency of buildings is a major barrier to alleviating the energy dilemma. Historically, monthly utility billing data was widely available and analytical methods for identifying building energy efficiency improvements, performing building Monitoring and Verification (M&V;) and continuous commissioning (CCx) were based on them. Although robust, these methods were not sensitive enough to detect a number of common causes for increased energy use. In recent years, prevalence of short-term building energy consumption data, also known as Energy Interval Data (EID), made available through the Smart Meters, along with data mining techniques presents the potential of knowledge discovery inherent in this data. This allows more sophisticated analytical tools to be developed resulting in greater sensitivities due to higher prediction accuracies; leading to deep energy savings and highly efficient building system operations. The research explores enhancements to Inverse Statistical Modeling techniques due to the availability of EID. Inverse statistical modeling is the process of identification of prediction model structure and estimates of model parameters. The methodology is based on several common statistical and data mining techniques: cluster analysis for day typing, outlier detection and removal, and generation of building scheduling. Inverse methods are simpler to develop and require fewer inputs for model identification. They can model changes in energy consumption based on changes in climatic variables and up to a certain extent, occupancy. This makes them easy-to-use and appealing to building managers for evaluating any general retrofits, building condition monitoring, continuous commissioning and short-term load forecasting (STLF). After evaluating several model structures, an elegant model form was derived which can be used to model daily energy consumption; which can be extended to model energy consumption for any specific hour by adding corrective terms. Additionally, adding AR terms to this model makes it usable for STLF. Two different buildings, one synthetic (ASHRAE medium-office prototype) building and another, an actual office building, were modeled using these techniques. The methodologies proposed have several novel features compared to the manner in which these models have been described earlier. Finally, this thesis investigates characteristic fault signature identification from detailed simulation models and subsequent inverse analysis.

Contributors

Agent

Created

Date Created
  • 2013

151008-Thumbnail Image.png

Adaptive operation decisions for a system of smart buildings

Description

Buildings (approximately half commercial and half residential) consume over 70% of the electricity among all the consumption units in the United States. Buildings are also responsible for approximately 40% of

Buildings (approximately half commercial and half residential) consume over 70% of the electricity among all the consumption units in the United States. Buildings are also responsible for approximately 40% of CO2 emissions, which is more than any other industry sectors. As a result, the initiative smart building which aims to not only manage electrical consumption in an efficient way but also reduce the damaging effect of greenhouse gases on the environment has been launched. Another important technology being promoted by government agencies is the smart grid which manages energy usage across a wide range of buildings in an effort to reduce cost and increase reliability and transparency. As a great amount of efforts have been devoted to these two initiatives by either exploring the smart grid designs or developing technologies for smart buildings, the research studying how the smart buildings and smart grid coordinate thus more efficiently use the energy is currently lacking. In this dissertation, a "system-of-system" approach is employed to develop an integrated building model which consists a number of buildings (building cluster) interacting with smart grid. The buildings can function as both energy consumption unit as well as energy generation/storage unit. Memetic Algorithm (MA) and Particle Swarm Optimization (PSO) based decision framework are developed for building operation decisions. In addition, Particle Filter (PF) is explored as a mean for fusing online sensor and meter data so adaptive decision could be made in responding to dynamic environment. The dissertation is divided into three inter-connected research components. First, an integrated building energy model including building consumption, storage, generation sub-systems for the building cluster is developed. Then a bi-level Memetic Algorithm (MA) based decentralized decision framework is developed to identify the Pareto optimal operation strategies for the building cluster. The Pareto solutions not only enable multiple dimensional tradeoff analysis, but also provide valuable insight for determining pricing mechanisms and power grid capacity. Secondly, a multi-objective PSO based decision framework is developed to reduce the computational effort of the MA based decision framework without scarifying accuracy. With the improved performance, the decision time scale could be refined to make it capable for hourly operation decisions. Finally, by integrating the multi-objective PSO based decision framework with PF, an adaptive framework is developed for adaptive operation decisions for smart building cluster. The adaptive framework not only enables me to develop a high fidelity decision model but also enables the building cluster to respond to the dynamics and uncertainties inherent in the system.

Contributors

Agent

Created

Date Created
  • 2012

153834-Thumbnail Image.png

Optimization of complex thermal-fluid processes

Description

First, in a large-scale structure, a 3-D CFD model was built to simulate flow and temperature distributions. The flow patterns and temperature distributions are characterized and validated through spot measurements.

First, in a large-scale structure, a 3-D CFD model was built to simulate flow and temperature distributions. The flow patterns and temperature distributions are characterized and validated through spot measurements. The detailed understanding of them then allows for optimization of the HVAC configuration because identification of the problematic flow patterns and temperature mis-distributions leads to some corrective measures. Second, an appropriate form of the viscous dissipation term in the integral form of the conservation equation was considered, and the effects of momentum terms on the computed drop size in pressure-atomized sprays were examined. The Sauter mean diameter (SMD) calculated in this manner agrees well with experimental data of the drop velocities and sizes. Using the suggested equation with the revised treatment of liquid momentum setup, injection parameters can be directly input to the system of equations. Thus, this approach is capable of incorporating the effects of injection parameters for further considerations of the drop and velocity distributions under a wide range of spray geometry and injection conditions. Lastly, groundwater level estimation was investigated using compressed sensing (CS). To satisfy a general property of CS, a random measurement matrix was used, the groundwater network was constructed, and finally the l-1 optimization was run. Through several validation tests, correct estimation of groundwater level by CS was shown. Using this setup, decreasing trends in groundwater level in the southwestern US was shown. The suggested method is effective in that the total measurements of registered wells can be reduced down by approximately 42 %, sparse data can be visualized and a possible approach for groundwater management during extreme weather changes, e.g. in California, was demonstrated.

Contributors

Agent

Created

Date Created
  • 2015