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A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.
Monitoring energy efficiency and greenhouse gas or fugitive emissions can directly contribute to environmental, social and governance (ESG) reporting, helping to manage and reduce the carbon footprint. These models often incorporate machinelearning and AI algorithms to detect the onset of degradation mechanisms in an early stage.
To address this issue, this work proposes an artificial intelligence (AI) empowered method based on the Environmental, Social, and Governance (ESG) big dataplatform, focusing on multi-objective scheduling optimization for clean energy.
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