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Full-Text Articles in Engineering
Self-Learning Algorithm To Predict Indoor Temperature And Cooling Demand From Smart Wifi Thermostat In A Residential Building, Kefan Huang, Kevin Hallinan, Robert Lou, Abdulrahman Alanezi, Salahaldin Alshatshati, Qiancheng Sun
Self-Learning Algorithm To Predict Indoor Temperature And Cooling Demand From Smart Wifi Thermostat In A Residential Building, Kefan Huang, Kevin Hallinan, Robert Lou, Abdulrahman Alanezi, Salahaldin Alshatshati, Qiancheng Sun
Mechanical and Aerospace Engineering Faculty Publications
Smart WiFi thermostats have moved well beyond the function they were originally designed for; namely, controlling heating and cooling comfort in buildings. They are now also learning from occupant behaviors and permit occupants to control their comfort remotely. This research seeks to go beyond this state of the art by utilizing smart WiFi thermostat data in residences to develop dynamic predictive models for room temperature and cooling/heating demand. These models can then be used to estimate the energy savings from new thermostat temperature schedules and estimate peak load reduction achievable from maintaining a residence in a minimum thermal comfort condition. …
A Machine Learning Framework For Drop-In Volume Swell Characteristics Of Sustainable Aviation Fuel, Shane Kosir, Joshua Heyne, John Graham
A Machine Learning Framework For Drop-In Volume Swell Characteristics Of Sustainable Aviation Fuel, Shane Kosir, Joshua Heyne, John Graham
Mechanical and Aerospace Engineering Faculty Publications
A machine learning framework has been developed to predict volume swell for 10 non-metallic materials submerged in neat compounds. The non-metallic materials included nitrile rubber, extracted nitrile rubber, fluorosilicone, low temp fluorocarbon, lightweight polysulfide, polythioether, epoxy (0.2 mm), epoxy (0.04 mm), nylon, and Kapton. Volume swell, a material compatibility concern, serves as a significant impediment for the minimization of the greenhouse gas emissions of aviation. Sustainable aviation fuels, the only near and mid-term solution to mitigating greenhouse gas emissions, are limited to low blend limits with conventional fuel due to material compatibility issues (i.e. O-ring swell). A neural network was …
Machine Learning Modeling Of Horizontal Photovoltaics Using Weather And Location Data, Christil Pasion, Torrey Wagner, Clay Koschnick, Steven Schuldt, Jada Williams, Kevin Hallinan
Machine Learning Modeling Of Horizontal Photovoltaics Using Weather And Location Data, Christil Pasion, Torrey Wagner, Clay Koschnick, Steven Schuldt, Jada Williams, Kevin Hallinan
Mechanical and Aerospace Engineering Faculty Publications
Solar energy is a key renewable energy source; however, its intermittent nature and potential for use in distributed systems make power prediction an important aspect of grid integration. This research analyzed a variety of machine learning techniques to predict power output for horizontal solar panels using 14 months of data collected from 12 northern-hemisphere locations. We performed our data collection and analysis in the absence of irradiation data-an approach not commonly found in prior literature. Using latitude, month, hour, ambient temperature, pressure, humidity, wind speed, and cloud ceiling as independent variables, a distributed random forest regression algorithm modeled the combined …