Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

AI-driven solutions in renewable energy: A review of data science applications in solar and wind energy optimization

View through CrossRef
This comprehensive review explores the transformative role of artificial intelligence (AI) and data science in the renewable energy sector, with a particular focus on solar and wind energy optimization. The study systematically examines the intersection of AI and renewable energy, highlighting the emergence of AI-driven solutions and their impact on enhancing the efficiency, reliability, and sustainability of renewable energy systems. The review begins with an overview of renewable energy and its growing importance in the global energy mix, emphasizing the critical role of AI in this sector. It then delves into the methodological approach, outlining the research strategy and criteria for selecting relevant AI and data science studies in renewable energy. This includes a detailed analysis of data collection and synthesis techniques used to identify key AI innovations and trends in solar and wind energy optimization. The core of the review comprises an extensive literature survey on AI applications in solar and wind energy systems. It covers fundamental principles of AI in renewable energy, state-of-the-art data science techniques, and emerging trends such as novel AI algorithms and their integration into renewable energy grids. The study evaluates the technological, economic, and environmental impacts of AI in renewable energy, addressing challenges and proposing solutions. Furthermore, the review discusses the role of standards and regulatory frameworks in AI-driven renewable energy and the implications for stakeholders. It concludes with a summary of AI's role in enhancing renewable energy, future prospects, and recommendations for industry leaders and policymakers. This review provides a thorough understanding of the current state and future potential of AI in renewable energy, offering valuable insights for researchers, industry professionals, and policymakers engaged in the field of sustainable energy.
Title: AI-driven solutions in renewable energy: A review of data science applications in solar and wind energy optimization
Description:
This comprehensive review explores the transformative role of artificial intelligence (AI) and data science in the renewable energy sector, with a particular focus on solar and wind energy optimization.
The study systematically examines the intersection of AI and renewable energy, highlighting the emergence of AI-driven solutions and their impact on enhancing the efficiency, reliability, and sustainability of renewable energy systems.
The review begins with an overview of renewable energy and its growing importance in the global energy mix, emphasizing the critical role of AI in this sector.
It then delves into the methodological approach, outlining the research strategy and criteria for selecting relevant AI and data science studies in renewable energy.
This includes a detailed analysis of data collection and synthesis techniques used to identify key AI innovations and trends in solar and wind energy optimization.
The core of the review comprises an extensive literature survey on AI applications in solar and wind energy systems.
It covers fundamental principles of AI in renewable energy, state-of-the-art data science techniques, and emerging trends such as novel AI algorithms and their integration into renewable energy grids.
The study evaluates the technological, economic, and environmental impacts of AI in renewable energy, addressing challenges and proposing solutions.
Furthermore, the review discusses the role of standards and regulatory frameworks in AI-driven renewable energy and the implications for stakeholders.
It concludes with a summary of AI's role in enhancing renewable energy, future prospects, and recommendations for industry leaders and policymakers.
This review provides a thorough understanding of the current state and future potential of AI in renewable energy, offering valuable insights for researchers, industry professionals, and policymakers engaged in the field of sustainable energy.

Related Results

Solar wind interaction with comet 67P around perihelion
Solar wind interaction with comet 67P around perihelion
AbstractNear perihelion, when comet 67P was most active, the Rosetta spacecraft resided inside the comet induced magnetosphere. The solar wind magnetic field was still present, but...
Introducing Optimal Energy Hub Approach in Smart Green Ports based on Machine Learning Methodology
Introducing Optimal Energy Hub Approach in Smart Green Ports based on Machine Learning Methodology
Abstract The integration of renewable energy systems in port facilities is essential for achieving sustainable and environmentally friendly operations. This paper presents ...
Wind lidars within Dutch offshore wind farms
Wind lidars within Dutch offshore wind farms
The growing number of wind farms in the Dutch part of the North Sea [1] offers the necessity, as well as the opportunity, to measure the meteorological conditions at these location...
The development of a cometosheath at comet 67P Churyumov-Gerasimenko
The development of a cometosheath at comet 67P Churyumov-Gerasimenko
Introduction: The Rosetta spacecraft followed the comet 67P/Churyumov-Gerasimenko for two years, through the atmosphere expanding and subsequently contracting with solar distance a...
Sustainable Energy Law: Origins and Power
Sustainable Energy Law: Origins and Power
Solar and wind renewable energy are now the fastest growing sources of new energy in the world. In the US, from 1984 to 2021, solar and wind increased by 24,691% to about 12% of cu...
Solar Trackers Using Six-Bar Linkages
Solar Trackers Using Six-Bar Linkages
Abstract A solar panel faces the sun or has the solar ray normal to its face to enhance power reaping. A fixed solar panel can only meet this condition at one moment...
Potential of Offshore Wind Energy in Australia
Potential of Offshore Wind Energy in Australia
Abstract Offshore wind energy has attracted great attention from numerous committees including governments, academics and engineers and energy companies due to gr...
Predictions of the solar wind speed by the probability distribution function model
Predictions of the solar wind speed by the probability distribution function model
AbstractThe near‐Earth space environment is strongly driven by the solar wind and interplanetary magnetic field. This study presents a model for predicting the solar wind speed up ...

Back to Top