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Leveraging Artificial Intelligence, Machine Learning, and Cloud-Based IT Integrations to Optimize Solar Power Systems and Renewable Energy Management

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Názov: Leveraging Artificial Intelligence, Machine Learning, and Cloud-Based IT Integrations to Optimize Solar Power Systems and Renewable Energy Management
Autori: Annapareddy, Venkata Narasareddy
Zdroj: SSRN Electronic Journal.
Informácie o vydavateľovi: Elsevier BV, 2025.
Rok vydania: 2025
Predmety: Artificial Intelligence, Machine Learning, • Solar Power Systems, Renewable Energy Management, Climate Change, Physical Systems, General Purpose, Control Methods, Intelligent Algorithms, Open Questions, Algorithm−Data Co-Design, Facilitators, Climate Engineering, Synthesis, Electricity Markets, System Availability, Intelligent Systems, Atmospheric Node
Popis: The global energy industry is experiencing a rapid energy transformation, changing the way we produce, manage, and consume energy. Electricity is a rapidly growing segment of the global energy system and the key enabler of decarbonization across several different sectors. An excess of renewable energy can lead to grid instability by making it difficult for the operator to balance electricity demand and generation. Market forces can enforce this balance, but these market solutions are often slow to deploy, reduce energy revenues, and can lead to curtailment rather than direct usage of excess sun. With energy forecasting and real-time analytics, the system can manage the dispatch of energy events, dynamically balancing load, generation, and energy storage. To extend system observability, machine learning-based forecasting and classification using telemetered grid conditions provide batch and real-time algorithms to evaluate grid conditions and outages.
Druh dokumentu: Article
Jazyk: English
ISSN: 1556-5068
DOI: 10.2139/ssrn.5244341
DOI: 10.5281/zenodo.15872362
DOI: 10.5281/zenodo.15872361
Rights: CC BY
Prístupové číslo: edsair.doi.dedup.....8f381caeb6b2574407b996f484fd1f02
Databáza: OpenAIRE
Popis
Abstrakt:The global energy industry is experiencing a rapid energy transformation, changing the way we produce, manage, and consume energy. Electricity is a rapidly growing segment of the global energy system and the key enabler of decarbonization across several different sectors. An excess of renewable energy can lead to grid instability by making it difficult for the operator to balance electricity demand and generation. Market forces can enforce this balance, but these market solutions are often slow to deploy, reduce energy revenues, and can lead to curtailment rather than direct usage of excess sun. With energy forecasting and real-time analytics, the system can manage the dispatch of energy events, dynamically balancing load, generation, and energy storage. To extend system observability, machine learning-based forecasting and classification using telemetered grid conditions provide batch and real-time algorithms to evaluate grid conditions and outages.
ISSN:15565068
DOI:10.2139/ssrn.5244341