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| Creator | Title | Description | Subject | Date | ||
|---|---|---|---|---|---|---|
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Smith, Amanda D. | Modeling two-phase flow and vapor cycles using the generalized fluid system simulation program | This work presents three new applications for the general purpose fluid network solver code GFSSP developed at NASA's Marshall Space Flight Center: (1) cooling tower, (2) vapor-compression refrigeration system, and (3) vapor-expansion power generation system. These systems are widely used across eng... | Modeling; Fluid systems; Cooling tower; Vapor compression refrigeration, vapor power cycle | 2017-09 |
| 2 |
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Thomas, Tran T. D. | Evaluation of renewable energy technologies and their potential for technical integration and cost-effective use within the U.S. energy sector | Energy demands, environmental impacts of energy conversion, and the depletion of fossil; fuels are constant topics of discussion in the energy industry. Renewable energy technologies; have been proposed for many years to address these concerns. However, the transformation; from traditional methods o... | Renewable energy; Power generation; Electrical grid; Emerging energy systems; System integration | 2017-07 |
| 3 |
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Tran, Thomas T. D. | System scaling approach and thermoeconomic analysis of a pressure retarded osmosis system for power production with hypersaline draw solution: A Great Salt Lake study | Osmotic power with pressure retarded osmosis (PRO) is an emerging renewable energy option for locations where fresh water and salt water mix. Energy can be recovered from the salinity gradient between the solutions. This study provides a comprehensive feasibility analysis for a PRO power plant in a ... | Pressure retarded osmosis; Power generation; Renewable energy; Hydroelectric; Levelized cost | 2017-06 |
| 4 |
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Rahman, Aowabin | Deep recurrent neural networks for building energy prediction | This poster illustrates the development of a deep recurrent neural network (RNN) model using long-short-term memory (LSTM) cells to predict energy consumption in buildings at one-hour time resolution over medium-to-long term time horizons ( greater than or equal to 1 week). | Machine learning; Energy; Building energy modeling; Deep learning; Recurrent neural networks; Prediction | 2017-01-13 |
| 5 |
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Rahman, Aowabin | Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks | This paper presents a recurrent neural network model to make medium-to-long term predictions, i.e. time horizon of ≥ 1 week, of electricity consumption profiles in commercial and residential buildings at one-hour resolution. Residential and commercial buildings are responsible for a significant fr... | Building Energy Modeling; Machine learning; Recurrent neural networks; Deep learning; Electric load prediction | 2017 |
| 6 |
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Bianchi, Carlo | Energy demands for commercial buildings with climate variability based on emission scenarios | The impacts of a changing climate are wide-ranging in both impact and scope. This paper investigates the effect that realistic climate variability would have on building energy demands in Salt Lake City, UT to inform planning for air quality impacts. Energy demand scenarios were derived using climat... | BEM; EnergyPlus; Emissions; Climate; Energy | 2017 |
| 7 |
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Rahman, Aowabin | Predicting fuel consumption for commercial building with machine learning algorithms | This paper presents a modeling framework that uses machine learning algorithms to make longterm, i.e. one year-ahead predictions, of fuel consumption in multiple types of commercial prototype buildings at one-hour resolutions. Weather and schedule variables were used as model inputs, and the hourly ... | Building energy modeling; Machine learning; Prediction; Heating load; Data-driven modeling | 2017-08 |
| 8 |
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Rashid, Khalid; Powell, Kody | Dynamic simulation, control, and design of a novel solar thermal hybrid power plant | Solar power is among the promising technologies leading towards cleaner fuel. However, there are still technological challenges regarding the reliability of power generation due to its intermittency. This work demonstrates the synergies that exist in integrated hybrid systems, where a dispatchable f... | Solar energy--Research; Solar thermal energy--Research; Solar power plants--Research | 2017 |
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