Methodology For Clustering High Resolution Spatiotemporal Solar Resource Data
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Methodology for Clustering High-Resolution Spatiotemporal Solar Resource Data
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.
Geographical Information Systems Theory, Applications and Management
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.
Deep Learning for Marine Science
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.