Guest Editors
Prof. Fausto Pedro Garcia Marquez
Email: faustopedro.garcia@uclm.es
Affiliation: Ingenium Research Group, University of Castilla-La Mancha, Calle Altagracia, Ciudad Real, 13071, 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, Edgbaston, Birmingham, B15 2TT, UK
Homepage:
Research Interests: artificial intelligence, maintenance, management, renewable energy, transport, advanced analytics, data science
Prof. Ren Vinicio Snchez Loja
Email: rsanchezl@ups.edu.ec
Affiliation: Research and Development Group in Industrial Technologies, Universidad Polotcnica Salesiana, Avenida 12 de Octubre N24-22 y Wilson, Quito, 170136, 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.
Here is a breakdown of how advanced analytics is applied in renewable energy:
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, big data for energy grid intelligence, energy storage performance and analytics, load forecasting in smart grids, grid optimization with AI and machine learning, energy market analysis and forecasting, AI-driven decision support in energy engineering, system modelling for renewable integration, demand response analytics for grid stability, decentralized energy systems and microgrid management
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