NEW WAYS OF IMPROVING OUTDATED METHODS AND TECHNOLOGIES

This paper presents RenewFusion-TFT, a Temporal Fusion Transformer (TFT)based framework for renewable energy forecasting.As the integration of renewable energy sources like wind and solar continues to expand, accurate forecasting is essential for ensuring grid stability, optimizing resource allocation, and enhancing energy market operations.Traditional forecasting models, such as ARIMA and Long Short-Term Memory (LSTM) networks, often struggle to capture the complex temporal dependencies and external covariates inherent in renewable energy systems.TFT, with its unique ability to handle both static and dynamic covariates while providing interpretability, offers a transformative solution.This study evaluates RenewFusion-TFT using diverse real-world datasets from wind farms and solar plants, achieving state-of-the-art performance in metrics such as Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE).The results underline the potential of TFT for renewable energy applications, enabling better integration of sustainable energy sources into modern energy systems and supporting global efforts toward decarbonization. Future Research DirectionsThe proposed RenewFusion-TFT framework provides a robust foundation for renewable energy forecasting but also opens several avenues for future exploration.One potential direction involves extending the framework to support real-time adaptive forecasting.By incorporating live data streams from operational energy systems, the model could dynamically adjust to evolving conditions, such as sudden weather changes or shifts in grid demand.Such real-time capabilities would be instrumental in maintaining the reliability of renewable energy grids under highly volatile conditions.Another area of interest lies in integrating external data sources to further enhance the model's predictive capabilities.Satellite imagery for solar irradiance estimation or high-resolution wind flow data could complement existing features, enabling finerscale predictions and improving performance in complex environmental scenarios.Additionally, coupling RenewFusion-TFT with optimization frameworks, such as unit commitment or economic dispatch models, could assist grid operators in minimizing operational costs while ensuring system reliability.Enhancing the interpretability of the framework is another promising avenue.Visualization tools that illustrate the contributions of different covariates over time could provide actionable insights for stakeholders, fostering greater trust and adoption of the model.Finally, extending the framework to multi-horizon forecasting would enable long-term planning, equipping utilities and policymakers with the tools necessary to anticipate seasonal trends and future energy demands.By addressing these directions, RenewFusion-TFT can evolve into a comprehensive solution for advancing renewable energy management and supporting the global transition toward sustainability.