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Erschienen in:
Introduction to Spatiotemporal Data Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

10. Spatiotemporal Renewable Energy Techniques and Applications

verfasst von : Abhishek Vyas, Satheesh Abimannan, Po-Ching Lin, Ren-Hung Hwang

Erschienen in: Spatiotemporal Data Analytics and Modeling

Verlag: Springer Nature Singapore

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Abstract

This chapter provides an overview of the field of spatiotemporal data analysis in the context of renewable energy applications. With the increasing use of renewable energy resources, more advanced and accurate analysis of spatiotemporal data is needed to optimize energy systems. The chapter discusses the importance, limitations, and applications of spatiotemporal data analytics, including predicting solar and wind energy production, analyzing the impact of weather events on renewable energy systems, and optimizing the placement and performance of renewable energy systems. One of the biggest challenges, however, is the volume and complexity of the data, which includes weather patterns, energy production, and use. Spatiotemporal data analytics for renewable energy applications is rapidly growing, but little is known about its potential benefits and limitations. More research is needed to fully understand how this technology can improve the reliability, efficiency, and sustainability of renewable energy systems. The chapter provides readers with a comprehensive view of the methodologies, algorithms, datasets, techniques, and frameworks used by various researchers in their state-of-the-art work on the applications of spatiotemporal data analytics in renewable energy systems. This chapter also discusses future research directions to improve the availability of renewable energy systems worldwide.

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Vorheriges Kapitel Spatio-Temporal Supply Chains and E-Commerce
Nächstes Kapitel Environmental Spatiotemporal Data Analytics
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Metadaten
Titel
Spatiotemporal Renewable Energy Techniques and Applications
verfasst von
Abhishek Vyas
Satheesh Abimannan
Po-Ching Lin
Ren-Hung Hwang
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Spatiotemporal Data Analytics and Modeling

Print ISBN: 978-981-9996-50-6

Electronic ISBN: 978-981-9996-51-3

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-99-9651-3_10

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