AI-Driven Energy Forecasting for Real-Time Renewable Grid Integration

There are two aspects to the fact that the modern power grid must grapple with the massive growth of the renewable sources of the energy. Although it is both environmentally clean and economically efficient, the intermittent and unpredictable nature of wind and solar generation poses the most crucial implementation challenges, in the context of both providing grid stability and reliability and the grid efficiency in general. To address this issue, this paper suggests the Federated Edge-Enhanced Hybrid AI (FEEHAI) System, a new real-time forecasting and combination framework, which was created with intelligent, large, and privacy-sensitive renewable grid controls in mind. The consequence is that FEEHAI incorporates a decentralized yet synergistic distributed architecture of edge computing that has three fundamental capabilities (federated learning, multi-modal data fusion, and federated learning). The opportunity to process the data on the edge nodes located within the grid infrastructure and obtain a rapid response in real time is provided by the fact that there are edge nodes embedded within the grid infrastructure. At the same time, federated learning standards facilitate training on multi-model on decentralized devices where user and infrastructure privacy is maintained and enhance the performance levels of prediction. Such a system combines multiple non-homogeneous data, such as weather sensors, satellite data, and past pattern of electricity use, present telemetry of the grid depending on an aggregate of AI that included deep neural networks and spatiotemporal models. The simulation results are further extrapolated to the extent that: FEEHAI performs better than the older counter parts, which are centralized and not federated in the aspects of the precision of the prediction and responsiveness to highly volatile parts. Besides, the architecture accommodates the dynamic scaling on regional scale and also grid-layer-by-grid-layer scale within the micro grids and national transmission system. The proposed FEEHAI System will guarantee the attainment of resilience reasons, real-time renewable energy activities since; the proposed system will have the capacity of carrying out AI driven intelligence of the edge level infrastructure that will make the system sustainable as far as the data privacy and system nimbleness is concerned.

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