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Uncertainty Quantification for Photo...

Hu, Weiming.

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Uncertainty Quantification for Photovoltaic Energy Production Using Analog Ensemble.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Uncertainty Quantification for Photovoltaic Energy Production Using Analog Ensemble./
Author:	Hu, Weiming.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	169 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
Subject:	Forecasting techniques. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28841695
ISBN:	9798460447640

Uncertainty Quantification for Photovoltaic Energy Production Using Analog Ensemble.
Hu, Weiming.

Uncertainty Quantification for Photovoltaic Energy Production Using Analog Ensemble. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 169 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--The Pennsylvania State University, 2021.

This item must not be sold to any third party vendors.

This dissertation focuses on using the Analog Ensemble and Machine Learning techniques to quantify power production uncertainty from photovoltaic solar and to improve prediction quality. Analog Ensemble is a technique to generate ensemble predictions using fewer computational resources than traditional ensemble prediction models. Its lower computational footprint allows a high resolution analysis over a large domain. This research extends and deepens scientific understand of the Analog Ensemble through the following subject areas: 1. Scalability: An efficient and scalable implementation of the Analog Ensemble is proposed and analyzed. It is used to quantify year-round hourly power production uncertainty throughout the Continental U.S. and to study the optimal configuration of solar panels. 2. Spatio-Temporal Weather Analogs: The weather similarity metric is renovated with a spatio-temporal neural network to incorporate crucial information when finding better weather analogs. 3. Model Interpretability: The neural network trained for weather analogs is "pried open" to show the learned information by the network and to better reason why a neural network outperforms the traditional weather similarity metric. Although the proposed method is applied and studied in the field of solar energy and weather forecasting, the knowledge is domain-independent with implications for other physical science subjects, where 1. operational forecasts and the observational archive are available; 2. quantifiable and justifiable measures of uncertainty are desired; 3. and effective management of computational resources is critical.

ISBN: 9798460447640Subjects--Topical Terms:

3564845
Forecasting techniques.
Subjects--Index Terms:

Solar energy

Uncertainty Quantification for Photovoltaic Energy Production Using Analog Ensemble.
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520 $a This dissertation focuses on using the Analog Ensemble and Machine Learning techniques to quantify power production uncertainty from photovoltaic solar and to improve prediction quality. Analog Ensemble is a technique to generate ensemble predictions using fewer computational resources than traditional ensemble prediction models. Its lower computational footprint allows a high resolution analysis over a large domain. This research extends and deepens scientific understand of the Analog Ensemble through the following subject areas: 1. Scalability: An efficient and scalable implementation of the Analog Ensemble is proposed and analyzed. It is used to quantify year-round hourly power production uncertainty throughout the Continental U.S. and to study the optimal configuration of solar panels. 2. Spatio-Temporal Weather Analogs: The weather similarity metric is renovated with a spatio-temporal neural network to incorporate crucial information when finding better weather analogs. 3. Model Interpretability: The neural network trained for weather analogs is "pried open" to show the learned information by the network and to better reason why a neural network outperforms the traditional weather similarity metric. Although the proposed method is applied and studied in the field of solar energy and weather forecasting, the knowledge is domain-independent with implications for other physical science subjects, where 1. operational forecasts and the observational archive are available; 2. quantifiable and justifiable measures of uncertainty are desired; 3. and effective management of computational resources is critical.
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