Author
Hugo T.C. Pedro
Other affiliations: University of California, Berkeley, University of Hawaii at Manoa, University of Hawaii
Bio: Hugo T.C. Pedro is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Solar irradiance & Irradiance. The author has an hindex of 22, co-authored 38 publications receiving 2925 citations. Previous affiliations of Hugo T.C. Pedro include University of California, Berkeley & University of Hawaii at Manoa.
Papers
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TL;DR: In this article, the authors review the theory behind these forecasting methodologies, and a number of successful applications of solar forecasting methods for both the solar resource and the power output of solar plants at the utility scale level.
813 citations
TL;DR: In this paper, the authors evaluate and compare several forecasting techniques using no exogenous inputs for predicting the solar power output of a 1MWp, single-axis tracking, photovoltaic power plant operating in Merced, California.
498 citations
TL;DR: This paper presents a preliminary study on how to review solar irradiance and photovoltaic power forecasting using text mining, which serves as the first part of a forthcoming series of text mining applications in solar forecasting.
348 citations
TL;DR: This study demonstrates the effectiveness of the optimized reforecasting method in reducing learnable errors produced by a diverse set of forecast methodologies.
186 citations
TL;DR: This work describes a new hybrid method that combines information from processed satellite images with Artificial Neural Networks (ANNs) for predicting global horizontal irradiance (GHI) at temporal horizons of 30, 60, 90, and 120 min.
170 citations
Cited by
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TL;DR: In this article, a new fractional derivative with non-local and no-singular kernel was proposed and applied to solve the fractional heat transfer model, and some useful properties of the new derivative were presented.
Abstract: In this manuscript we proposed a new fractional derivative with non-local and
no-singular kernel. We presented some useful properties of the new derivative
and applied it to solve the fractional heat transfer model.
2,364 citations
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TL;DR: In this paper, a new fractional derivative with non-local and no-singular kernel was proposed and applied to solve the fractional heat transfer model, and some useful properties of the new derivative were presented.
Abstract: In this manuscript we proposed a new fractional derivative with non-local and no-singular kernel. We presented some useful properties of the new derivative and applied it to solve the fractional heat transfer model.
1,372 citations
TL;DR: An overview of forecasting methods of solar irradiation using machine learning approaches is given and it will be shown that other methods begin to be used in this context of prediction.
1,095 citations
TL;DR: Topology optimization is the process of determining the optimal layout of material and connectivity inside a design domain this paper, which is the same as the problem of finding the optimal configuration of a set of components.
Abstract: Topology optimization is the process of determining the optimal layout of material and connectivity inside a design domain. This paper surveys topology optimization of continuum structures from the year 2000 to 2012. It focuses on new developments, improvements, and applications of finite element-based topology optimization, which include a maturation of classical methods, a broadening in the scope of the field, and the introduction of new methods for multiphysics problems. Four different types of topology optimization are reviewed: (1) density-based methods, which include the popular Solid Isotropic Material with Penalization (SIMP) technique, (2) hard-kill methods, including Evolutionary Structural Optimization (ESO), (3) boundary variation methods (level set and phase field), and (4) a new biologically inspired method based on cellular division rules. We hope that this survey will provide an update of the recent advances and novel applications of popular methods, provide exposure to lesser known, yet promising, techniques, and serve as a resource for those new to the field. The presentation of each method's focuses on new developments and novel applications.
1,052 citations
TL;DR: This paper appears with the aim of compiling a large part of the knowledge about solar power forecasting, focusing on the latest advancements and future trends, and represents the most up-to-date compilation of solarPower forecasting studies.
829 citations