國立虎尾科技大學 |

A Framework for Signal Decomposition with Applications to Solar Energy Generation.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	A Framework for Signal Decomposition with Applications to Solar Energy Generation./
Author:	Meyers, Bennet E.
Description:	1 online resource (126 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
Subject:	Energy. -
Online resource:	click for full text (PQDT)
ISBN:	9798379649067

A Framework for Signal Decomposition with Applications to Solar Energy Generation.
Meyers, Bennet E.

A Framework for Signal Decomposition with Applications to Solar Energy Generation. - 1 online resource (126 pages)

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Thesis (Ph.D.)--Stanford University, 2023.

Includes bibliographical references

My research has been focused on two major areas: (1) optimization as a methodology for signal decomposition and (2) developing useful, practical methods for solar power data science. This dissertation is a synthesis of these areas of research.I present a generalized framework for decomposing time series signal into components, which is motivated by challenges in the analysis of photo-voltaic (PV) data. The industry standard approach uses PV output time series plus additional time series measuring local weather conditions (e.g., irradiance, temperature, and wind speed) and system configuration/modeling parameters (e.g., location, mounting, and orientation). However, it can be very difficult (sometimes impossible) to gather all this data. And so, we propose performing PV performance analysis using only PV output time series, through the application of signal decomposition.Chapters 2 through 6 are based on [73], in which we consider the well-studied problem of decomposing a vector time series signal into components with different characteristics, such as smooth, periodic, nonnegative, or sparse. We describe a simple and general framework in which the components are defined by loss functions (which include constraints), and the signal decomposition is carried out by minimizing the sum of losses of the components (subject to the constraints). When each loss function is the negative log-likelihood of a density for the signal component, this framework coincides with maximum a posteriori probability (MAP) estimation; but it also includes many other interesting cases. Summarizing and clarifying prior results, we give two distributed optimization methods for computing the decomposition, which find the optimal decomposition when the component class loss functions are convex, and are good heuristics when they are not. Both methods require only themasked proximal operator of each of the component loss functions, a generalization of the well-known proximal operator that handles missing entries in its argument. Both methods are distributed, i.e., handle each component separately. We derive tractable methods for evaluating the masked proximal operators of some loss functions that, to our knowledge, have not appeared in the literature. Chapter 6 is concerned with numerical examples from three different domains.Chapter 7 is based on [69]. In this chapter, we provide a methodology for estimating the losses due to soiling for PV systems. We focus this work on estimating the losses from historical power production data that are unlabeled, i.e. power measurements with time stamps, but no other information such as site configuration or meteorological data. We present a validation of this approach on a small fleet of typical rooftop PV systems. The proposed method differs from prior work in that the construction of a performance index is not required to analyze soiling loss. This approach is appropriate for analyzing the soiling losses in field production data from fleets of distributed rooftop systems and is highly automatic, allowing for scaling to large fleets of heterogeneous PV systems.Chapter 8 is based on [74]. In this chapter, we provide a methodology for estimating the losses due to shade in power generation data sets produced by real-world PV systems. We focus this work on estimating shade loss from data that are unlabeled, i.e. power measurements with time stamps but no other information such as site configuration or meteorological data. This approach enables, for the first time, the analysis of data generated by small scale, distributed PV systems, which do not have the data quality or richness of large, utility-scale PV systems or research-grade installations.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379649067Subjects--Topical Terms:

784773
Energy.
Index Terms--Genre/Form:

554714
Electronic books.

A Framework for Signal Decomposition with Applications to Solar Energy Generation.
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504 $a Includes bibliographical references
520 $a My research has been focused on two major areas: (1) optimization as a methodology for signal decomposition and (2) developing useful, practical methods for solar power data science. This dissertation is a synthesis of these areas of research.I present a generalized framework for decomposing time series signal into components, which is motivated by challenges in the analysis of photo-voltaic (PV) data. The industry standard approach uses PV output time series plus additional time series measuring local weather conditions (e.g., irradiance, temperature, and wind speed) and system configuration/modeling parameters (e.g., location, mounting, and orientation). However, it can be very difficult (sometimes impossible) to gather all this data. And so, we propose performing PV performance analysis using only PV output time series, through the application of signal decomposition.Chapters 2 through 6 are based on [73], in which we consider the well-studied problem of decomposing a vector time series signal into components with different characteristics, such as smooth, periodic, nonnegative, or sparse. We describe a simple and general framework in which the components are defined by loss functions (which include constraints), and the signal decomposition is carried out by minimizing the sum of losses of the components (subject to the constraints). When each loss function is the negative log-likelihood of a density for the signal component, this framework coincides with maximum a posteriori probability (MAP) estimation; but it also includes many other interesting cases. Summarizing and clarifying prior results, we give two distributed optimization methods for computing the decomposition, which find the optimal decomposition when the component class loss functions are convex, and are good heuristics when they are not. Both methods require only themasked proximal operator of each of the component loss functions, a generalization of the well-known proximal operator that handles missing entries in its argument. Both methods are distributed, i.e., handle each component separately. We derive tractable methods for evaluating the masked proximal operators of some loss functions that, to our knowledge, have not appeared in the literature. Chapter 6 is concerned with numerical examples from three different domains.Chapter 7 is based on [69]. In this chapter, we provide a methodology for estimating the losses due to soiling for PV systems. We focus this work on estimating the losses from historical power production data that are unlabeled, i.e. power measurements with time stamps, but no other information such as site configuration or meteorological data. We present a validation of this approach on a small fleet of typical rooftop PV systems. The proposed method differs from prior work in that the construction of a performance index is not required to analyze soiling loss. This approach is appropriate for analyzing the soiling losses in field production data from fleets of distributed rooftop systems and is highly automatic, allowing for scaling to large fleets of heterogeneous PV systems.Chapter 8 is based on [74]. In this chapter, we provide a methodology for estimating the losses due to shade in power generation data sets produced by real-world PV systems. We focus this work on estimating shade loss from data that are unlabeled, i.e. power measurements with time stamps but no other information such as site configuration or meteorological data. This approach enables, for the first time, the analysis of data generated by small scale, distributed PV systems, which do not have the data quality or richness of large, utility-scale PV systems or research-grade installations.
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