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You searched for +publisher:"University of Arizona" +contributor:("Morzfeld, Matthias"). Showing records 1 – 2 of 2 total matches.

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University of Arizona

1. Lorenzo, Antonio Tomas. Short-Term Irradiance Forecasting Using an Irradiance Monitoring Network, Satellite Imagery, and Data Assimilation .

Degree: 2017, University of Arizona

Solar and other renewable power sources are becoming an integral part of the electrical grid in the United States. In the Southwest US, solar and wind power plants already serve over 20% of the electrical load during the daytime on sunny days in the Spring. While solar power produces fewer emissions and has a lower carbon footprint than burning fossil fuels, solar power is only generated during the daytime and it is variable due to clouds blocking the sun. Electric utilities that are required to maintain a reliable electricity supply benefit from anticipating the schedule of power output from solar power plants. Forecasting the irradiance reaching the ground, the primary input to a solar power forecast, can help utilities understand and respond to the variability. This dissertation will explore techniques to forecast irradiance that make use of data from a network of sensors deployed throughout Tucson, AZ. The design and deployment of inexpensive sensors used in the network will be described. We will present a forecasting technique that uses data from the sensor network and outperforms a reference persistence forecast for one minute to two hours in the future. We will analyze the errors of this technique in depth and suggest ways to interpret these errors. Then, we will describe a data assimilation technique, optimal interpolation, that combines estimates of irradiance derived from satellite images with data from the sensor network to improve the satellite estimates. These improved satellite estimates form the base of future work that will explore generating forecasts while continuously assimilating new data. Advisors/Committee Members: Cronin, Alexander D (advisor), Morzfeld, Matthias (advisor), Cronin, Alexander D. (committeemember), Morzfeld, Matthias (committeemember), Potter, Barrett G. (committeemember).

Subjects/Keywords: data assimilation; forecasting; sensor network; solar power

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lorenzo, A. T. (2017). Short-Term Irradiance Forecasting Using an Irradiance Monitoring Network, Satellite Imagery, and Data Assimilation . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/624494

Chicago Manual of Style (16th Edition):

Lorenzo, Antonio Tomas. “Short-Term Irradiance Forecasting Using an Irradiance Monitoring Network, Satellite Imagery, and Data Assimilation .” 2017. Doctoral Dissertation, University of Arizona. Accessed February 19, 2019. http://hdl.handle.net/10150/624494.

MLA Handbook (7th Edition):

Lorenzo, Antonio Tomas. “Short-Term Irradiance Forecasting Using an Irradiance Monitoring Network, Satellite Imagery, and Data Assimilation .” 2017. Web. 19 Feb 2019.

Vancouver:

Lorenzo AT. Short-Term Irradiance Forecasting Using an Irradiance Monitoring Network, Satellite Imagery, and Data Assimilation . [Internet] [Doctoral dissertation]. University of Arizona; 2017. [cited 2019 Feb 19]. Available from: http://hdl.handle.net/10150/624494.

Council of Science Editors:

Lorenzo AT. Short-Term Irradiance Forecasting Using an Irradiance Monitoring Network, Satellite Imagery, and Data Assimilation . [Doctoral Dissertation]. University of Arizona; 2017. Available from: http://hdl.handle.net/10150/624494


University of Arizona

2. Leach, Andrew Bradford. Monte Carlo Methods for Stochastic Differential Equations and their Applications .

Degree: 2017, University of Arizona

We introduce computationally efficient Monte Carlo methods for studying the statistics of stochastic differential equations in two distinct settings. In the first, we derive importance sampling methods for data assimilation when the noise in the model and observations are small. The methods are formulated in discrete time, where the "posterior" distribution we want to sample from can be analyzed in an accessible small noise expansion. We show that a "symmetrization" procedure akin to antithetic coupling can improve the order of accuracy of the sampling methods, which is illustrated with numerical examples. In the second setting, we develop "stochastic continuation" methods to estimate level sets for statistics of stochastic differential equations with respect to their parameters. We adapt Keller's Pseudo-Arclength continuation method to this setting using stochastic approximation, and generalized least squares regression. Furthermore, we show that the methods can be improved through the use of coupling methods to reduce the variance of the derivative estimates that are involved. Advisors/Committee Members: Lin, Kevin (advisor), Morzfeld, Matthias (advisor), Lin, Kevin (committeemember), Morzfeld, Matthias (committeemember), Lega, Joceline (committeemember), Sethuraman, Sunder (committeemember), Venkataramani, Shankar (committeemember).

Subjects/Keywords: Continuation; Gaussian Approximation; Importance Sampling; Monte Carlo Methods; Stochastic Approximation; Stochastic Differential Equations

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Leach, A. B. (2017). Monte Carlo Methods for Stochastic Differential Equations and their Applications . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/624570

Chicago Manual of Style (16th Edition):

Leach, Andrew Bradford. “Monte Carlo Methods for Stochastic Differential Equations and their Applications .” 2017. Doctoral Dissertation, University of Arizona. Accessed February 19, 2019. http://hdl.handle.net/10150/624570.

MLA Handbook (7th Edition):

Leach, Andrew Bradford. “Monte Carlo Methods for Stochastic Differential Equations and their Applications .” 2017. Web. 19 Feb 2019.

Vancouver:

Leach AB. Monte Carlo Methods for Stochastic Differential Equations and their Applications . [Internet] [Doctoral dissertation]. University of Arizona; 2017. [cited 2019 Feb 19]. Available from: http://hdl.handle.net/10150/624570.

Council of Science Editors:

Leach AB. Monte Carlo Methods for Stochastic Differential Equations and their Applications . [Doctoral Dissertation]. University of Arizona; 2017. Available from: http://hdl.handle.net/10150/624570

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