Consistently accurate forecasts of temperature within buildings from sensor data using ridge and lasso regression

© 2018 Elsevier B.V. A significant portion of all energy generated is used to heat and cool buildings. Some of that energy can be saved by using a temperature controller with access to an accurate forecast of a building\u27s internal temperature. These forecasts depend on information gathered from sensors, including temperature, humidity, sunlight, and the electrical load of cooking and laundry appliances. Using publicly available data from two homes with a wide variety of sensors, we forecast internal temperature by modelling it as a linear function of recent sensor values. These models are built using techniques that improve upon standard least squares regression: forward stepwise, ridge and lasso regressions, using cross-validation. With lasso regression, we accurately forecast internal temperature every quarter hour over the next 48 h within 1.8°C in both houses. We also forecast temperature changes over each quarter-hour for the next two days, within 0.05°C, which significantly improves on previous forecasts of temperature changes using the same data. We propose a business model for forecasting as a service, where guarantees of consistent accuracy are important for attracting clients and saving energy

Electrical control and enhancement of dropwise condensation

Condensation of vapor typically occurs via the formation of condensate films on condensing surfaces; however, the liquid film imposes a substantial thermal resistance to heat transfer. Filmwise condensation heat transfer can be enhanced by 5-7X by condensing vapor as droplets, which roll-off the surface, thereby preventing buildup of a liquid film. Dropwise condensation heat transfer can be enhanced by the use of electrowetting (EW) to enhance coalescence, growth and shedding of condensed droplets. This dissertation includes several fundamental studies on EW-enhanced dropwise condensation. Experiments, analytical modeling and statistical modeling are used to gain a deeper understanding of droplet growth, coalescence and shedding under EW. Chapter 1 details the motivation for this study and the objectives of this dissertation. Chapter 2 includes a literature review of condensation, electrowetting and data science- based statistical methods. Chapter 3 presents a detailed experimental study of dropwise condensation of humid air under the influence of electrowetting fields. An analytical heat transfer model, which accounts for the presence of non-condensable gases, is used to predict the heat transfer benefits associated with electrowetting-assisted condensation. Chapter 4 presents a detailed analysis of electrowetting-induced coalescence dynamics of a distribution of water droplets. Statistical modeling-based algorithms are used to identify key electrowetting-related parameters that influence droplet coalescence; the influence of these parameters on coalescence is quantified. Chapter 5 studies droplet shedding dynamics under electrowetting and shows that an intermittent electric field can significantly increase condensation rates (as compared to a continuous electric field). A key finding is the almost complete removal of water from surfaces in very short durations (< 1 sec) is observed. It is also found that the extent and rate of water removal depends on the applied voltage and frequency of the AC EW waveform, respectively. Chapter 6 presents a novel approach and an experimentally validated model to analyze the oscillations of water droplets under the influence of AC electrowetting. Chapter 7 summarizes key conclusions and outlines suggestions for future work. Overall, the research reported in this dissertation has led to fundamental contributions in the areas of condensation and microfluidics. This multidisciplinary work has involved experiments, analytical modeling and statistical modeling. Results show that electrowetting fields influence all the phenomena important in dropwise condensation (growth, coalescence, shedding of droplets). Electrowetting is therefore a powerful tool to control and enhance condensation heat transfer. This research impacts applications in energy (steam condensation, refrigeration), water (atmospheric water harvesting, desalination) and infrastructure (self-cleaning).Mechanical Engineerin