Guest Editor(s)
Prof. Fausto Pedro Garcia Marquez
Email: faustopedro.garcia@uclm.es
Affiliation: Ingenium Research Group, Universidad de Castilla-La Mancha, Spain
Homepage:
Research Interests: artificial intelligence, maintenance, management, renewable energy, transport, advanced analytics, data science
Prof. Mayorkinos Papaelias
Email: m.papaelias@bham.ac.uk
Affiliation: School of Metallurgy and Materials, University of Birmingham, Birmingham, United Kingdom
Homepage:
Research Interests: artificial intelligence, maintenance, management, renewable energy, transport, advanced analytics, data science
Prof. René Vinicio Sánchez Loja
Email: rsanchezl@ups.edu.ec
Affiliation: Grupo de Investigación y Desarrollo en Tecnologías Industriales (GIDTEC), Universidad Polotécnica Salesiana, Ecuador
Homepage:
Research Interests: renewable energy, analytics, project management, condition-based maintenance, engineering education, Industry 4.0
Summary
Advanced Analytics Insights in Renewable Energy refers to the use of sophisticated data analysis techniques to derive actionable knowledge and insights from large and complex datasets in the renewable energy sector. By leveraging technologies such as artificial intelligence (AI), machine learning (ML), big data, and predictive analytics, organizations can optimize the performance, efficiency, and integration of renewable energy systems.
Suggested themes include, but are not limited to:
1. Performance Optimization
Wind Turbines: Sensors on systems collect data. Advanced analytics helps optimize the system production.
2. Engineering and Forecasting
Analytics algorithms can monitor equipment health and predict when components are likely to fail. This reduces downtime and lowers maintenance costs by enabling proactive repairs before a breakdown occurs.
3. System Integration and Management
Load Forecasting: Machine learning models analyze historical demand and patterns to predict future consumption. This helps grid operators balance supply from sources with demand.
Demand Response: Advanced analytics enables smart systems to respond to fluctuations in demand by automatically adjusting energy output from system sources.
4. Engineering on Trading and Market Forecasting
Data analytics helps traders and producers optimize when to sell or store based on real-time market conditions, price predictions, and forecasts of production.
5. Environmental and Financial Impact Assessment
Advanced analytics can model and predict the long-term environmental benefits (like reduced carbon emissions) and financial returns of renewable energy investments, aiding decision-making for investors and policymakers.
6. Distributed Resources (DER) Management
For decentralized systems, advanced analytics enables real-time monitoring and control, ensuring efficient use of distributed resources.
In summary, Advanced Analytics Insights are vital in making systems more efficient, cost-effective, reliable, and sustainable by providing deeper data-driven decision-making capabilities across all facets of the lifecycle.
Keywords
energy system forecasting and simulation, engineering data analytics for renewable energy, smart energy systems and IoT integration, machine learning for energy management optimization, predictive maintenance in power systems, engineering optimization for energy networks, energy consumption patterns and behavioral analysis, real-time monitoring of energy infrastructures, wind power forecasting and performance analytics, system efficiency in distributed energy resources
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