Use of AI in smart grids
Source: AzoAI / Samudrapom Dam
Samudrapom Dam suggests that AI enables optimized energy processes, predictive maintenance, and efficient demand-side management in smart grids, enhancing energy system resilience and sustainability.
However, challenges such as the complexity of integrating AI into existing grid infrastructures, data privacy concerns, and limitations in AI techniques need to be addressed.
Despite these challenges, AI applications in smart grids offer opportunities for more accurate forecasting, sophisticated alerts on outages, optimized power yield, improved automated switching, flexible demand-side management, enhanced cybersecurity, lower costs, and fewer outages.
Additionally, AI techniques find applications in load forecasting, power grid stability assessment, fault detection, and smart grid security, offering promising solutions to enhance grid reliability, efficiency, and resilience.
Looking ahead, integrating AI with cloud computing and leveraging transfer learning to address data challenges represent promising avenues for the future development of AI-enabled smart grid systems.