Statements (53)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
application of artificial intelligence
|
| gptkbp:appliesTo |
gptkb:energy
|
| gptkbp:challenge |
gptkb:legislation
cybersecurity data privacy scalability data quality integration with legacy systems high initial investment lack of skilled workforce |
| gptkbp:enables |
automation
data-driven decision making real-time monitoring |
| gptkbp:example |
gptkb:AutoGrid
gptkb:Google_DeepMind_for_energy_savings gptkb:IBM_Watson_for_energy gptkb:Schneider_Electric_EcoStruxure gptkb:Siemens_MindSphere |
| gptkbp:goal |
reduce carbon emissions
improve reliability reduce energy costs optimize resource allocation increase sustainability |
| https://www.w3.org/2000/01/rdf-schema#label |
AI for Energy
|
| gptkbp:relatedTo |
gptkb:machine_learning
gptkb:Internet_of_Things deep learning energy storage smart meters big data analytics digital twins distributed energy resources |
| gptkbp:usedBy |
gptkb:government_agency
utilities industrial facilities research institutions energy companies grid operators building managers renewable energy providers |
| gptkbp:usedFor |
energy efficiency
asset management energy management load balancing fault detection predictive maintenance renewable energy integration smart grids energy trading demand forecasting grid optimization |
| gptkbp:bfsParent |
gptkb:German_Research_Center_for_Artificial_Intelligence_(DFKI)
|
| gptkbp:bfsLayer |
5
|