Methodology for Clustering High-Resolution Spatiotemporal Solar Resource Data PDF Download
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Author: Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
We introduce a methodology to achieve multiple levels of spatial resolution reduction of solar resource data, with minimal impact on data variability, for use in energy systems modeling. The selection of an appropriate clustering algorithm, parameter selection including cluster size, methods of temporal data segmentation, and methods of cluster evaluation are explored in the context of a repeatable process. In describing this process, we illustrate the steps in creating a reduced resolution, but still viable, dataset to support energy systems modeling, e.g. capacity expansion or production cost modeling. This process is demonstrated through the use of a solar resource dataset; however, the methods are applicable to other resource data represented through spatiotemporal grids, including wind data. In addition to energy modeling, the techniques demonstrated in this paper can be used in a novel top-down approach to assess renewable resources within many other contexts that leverage variability in resource data but require reduction in spatial resolution to accommodate modeling or computing constraints.
Author: Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
We introduce a methodology to achieve multiple levels of spatial resolution reduction of solar resource data, with minimal impact on data variability, for use in energy systems modeling. The selection of an appropriate clustering algorithm, parameter selection including cluster size, methods of temporal data segmentation, and methods of cluster evaluation are explored in the context of a repeatable process. In describing this process, we illustrate the steps in creating a reduced resolution, but still viable, dataset to support energy systems modeling, e.g. capacity expansion or production cost modeling. This process is demonstrated through the use of a solar resource dataset; however, the methods are applicable to other resource data represented through spatiotemporal grids, including wind data. In addition to energy modeling, the techniques demonstrated in this paper can be used in a novel top-down approach to assess renewable resources within many other contexts that leverage variability in resource data but require reduction in spatial resolution to accommodate modeling or computing constraints.
Author: Cédric Grueau Publisher: Springer Nature ISBN: 3031441125 Category : Computers Languages : en Pages : 128
Book Description
This book constitutes selected, revised and extended papers of the 7th International Conference on Geographical Information Systems Theory, Applications and Management, GISTAM 2021, held in April 2021, and 8th International Conference on Geographical Information Systems Theory, Applications and Management, GISTAM 2022, held in April 2022. Due to the COVID-19 pandemic both conferences were held online. The 4 revised full papers presented at GISTAM 2021 were carefully reviewed and selected from the 44 submissions, and 3 papers presented at GISTAM 2022 were selected from the 27 submissions. The papers are centered on urban and regional planning; water information systems; geospatial information and technologies; spatio-temporal database management; decision support systems; energy information systems; GPS and location detection.
Author: Jakub Jurasz Publisher: Academic Press ISBN: 0323855288 Category : Technology & Engineering Languages : en Pages : 746
Book Description
Complementarity of Variable Renewable Energy Sources consolidates current developments on the subject, addressing all technical advances, presenting new mapping results, and bringing new insights for the continuation of research and implementation on this fascinating topic. By answering questions such as How can complementarity be used in the operation of large interconnected systems?, What is the real applicability potential of energetic complementarity?, and How will it impact energy generation systems?, this title is useful for all researchers, academic and students investigating the topic of renewable energy complementarity in systems. In just over a decade, the subject of 'energy complementarity' has experienced a growing presence and understanding by researchers and managers of energy resources looking to enhance energy systems. Early research proposed methods to quantify complementarity, the effects of complementarity on performance of hybrid systems, and how to identify and map complementarity between solar energy, wind energy and hydroelectric energy systems. Includes chapter maps to visualize system performance under different complementarity indexes Addresses complementarity in the operation of large and small to medium-sized hybrid systems Provides methods for determining complementarity between various energy sources
Author: Haiyong Zheng Publisher: Frontiers Media SA ISBN: 2832549055 Category : Science Languages : en Pages : 555
Book Description
Deep learning (DL), mainly composed of deep and complex neural networks such as recurrent network and convolutional network, is an emerging research branch in the field of artificial intelligence and machine learning. DL revolution has a far-reaching impact on all scientific disciplines and every corner of our lives. With continuing technological advances, marine science is entering into the big data era with the exponential growth of information. DL is an effective means of harnessing the power of big data. Combined with unprecedented data from cameras, acoustic recorders, satellite remote sensing, and large model outputs, DL enables scientists to solve complex problems in biology, ecosystems, climate, energy, as well as physical and chemical interactions. Although DL has made great strides, it is still only beginning to emerge in many fields of marine science, especially towards representative applications and best practices for the automatic analysis of marine organisms and marine environments. DL in nowadays' marine science mainly leverages cutting-edge techniques of deep neural networks and massive data which collected by in-situ optical or acoustic imaging sensors for underwater applications, such as plankton classification and coral reef detection. This research topic aims to expand the applications of marine science to cover all aspects of detection, classification, segmentation, localization, and density estimation of marine objects, organisms, and phenomena.
Author: Publisher: ISBN: Category : Languages : en Pages :
Book Description
Data mining aims to discover patterns and extract useful information recorded in databases. Spatial data mining and temporal data mining are two important branches that deal with location data and time series data respectively. Several researchers have studied either spatial data mining or temporal data mining and have proposed algorithms to find clusters. The integration of both spatial and temporal data mining leads to spatio-temporal data mining that deals with the discovery of spatial and temporal relationships. In this thesis, a novel approach is discussed to discover spatio-temporal clusters or patterns of similar characteristics. Regions of similar characteristics in spatio-temporal databases are discovered. The approach considered in this thesis translates each profile into a symbolic sequence and constructs a Generalized Suffix Tree (GST) of all subsequences that are shared by at least two sequences. GST implementation is used for representing multiple sequences and searching patterns in them. The proposed algorithm clusters the profiles which share the same set of subsequences based on temporal hypothesis. To generate more general hypotheses about temporal behavior, the subsequences that define each cluster are generalized. The profiles generated after generalization are further clustered based on a metric ratio. These clusters of temporal subsequences based on spatial hypothesis result in spatio-temporal clustering and help in discovering patterns of similar characteristics. To test and validate the proposed algorithm, different datasets are considered. Details of the implementation and results are provided in the thesis.
Author: Gernot Stoeglehner Publisher: Springer ISBN: 3319318705 Category : Business & Economics Languages : en Pages : 126
Book Description
This book focuses on spatial planning – an important determinant of energy saving and renewable energy supply. Revealing the key driving forces for spatial development supporting the shift towards energy efficiency and renewable energy supplies, it shows the importance of integrated spatial and energy planning approaches for a timely and sustainable change of energy systems, thus supporting policies of climate protection. As operating within the context of renewable energy sources is becoming a major policy issue at the international, European and national level, spatial dimensions of renewable energy systems as well as challenges, barriers and opportunities in different spatial contexts become more important. This book analyses not only the fundamental system interrelations between resources, technologies and consumption patterns with respect to energy, but also the links to the spatial context, and provides guidelines for researchers as well as practitioners in this new, emerging field. It presents innovative analytical tools to solve real-world problems and discusses the most important fields of action in integrated spatial and energy planning including planning contents, planning visions and principles as well as planning process design and planning methodology.
Author: Shuai Liu Publisher: Springer Nature ISBN: 303063955X Category : Education Languages : en Pages : 363
Book Description
This 2-volume set constitutes the proceedings of the 6th International Conference on e-Learning, e-Education, and Online Training, eLEOT 2020, held in Changsha, China, in June 2020. The conference was held virtually due to the COVID-19 pandemic. The 68 full papers presented were carefully reviewed and selected from 141 submissions. They focus on most recent and innovative trends and new technologies in for educational modernization, such as artificial intelligence and big data. The theme of eLEOT 2020 was “Education with New Generation Information Technology”.
Author: Jan Kleissl Publisher: Academic Press ISBN: 012397772X Category : Technology & Engineering Languages : en Pages : 503
Book Description
Solar Energy Forecasting and Resource Assessment is a vital text for solar energy professionals, addressing a critical gap in the core literature of the field. As major barriers to solar energy implementation, such as materials cost and low conversion efficiency, continue to fall, issues of intermittency and reliability have come to the fore. Scrutiny from solar project developers and their financiers on the accuracy of long-term resource projections and grid operators’ concerns about variable short-term power generation have made the field of solar forecasting and resource assessment pivotally important. This volume provides an authoritative voice on the topic, incorporating contributions from an internationally recognized group of top authors from both industry and academia, focused on providing information from underlying scientific fundamentals to practical applications and emphasizing the latest technological developments driving this discipline forward. The only reference dedicated to forecasting and assessing solar resources enables a complete understanding of the state of the art from the world’s most renowned experts. Demonstrates how to derive reliable data on solar resource availability and variability at specific locations to support accurate prediction of solar plant performance and attendant financial analysis. Provides cutting-edge information on recent advances in solar forecasting through monitoring, satellite and ground remote sensing, and numerical weather prediction.
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Author: Grant Buster
Author: Cédric Grueau
Author: Jakub Jurasz
Author: Haiyong Zheng
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Author: Gernot Stoeglehner
Author: Shuai Liu
Author: Jan Kleissl
Author: M. Sengupta